[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-ChandlerBang--awesome-self-supervised-gnn":3,"tool-ChandlerBang--awesome-self-supervised-gnn":64},[4,17,27,35,43,56],{"id":5,"name":6,"github_repo":7,"description_zh":8,"stars":9,"difficulty_score":10,"last_commit_at":11,"category_tags":12,"status":16},3808,"stable-diffusion-webui","AUTOMATIC1111\u002Fstable-diffusion-webui","stable-diffusion-webui 是一个基于 Gradio 构建的网页版操作界面，旨在让用户能够轻松地在本地运行和使用强大的 Stable Diffusion 图像生成模型。它解决了原始模型依赖命令行、操作门槛高且功能分散的痛点，将复杂的 AI 绘图流程整合进一个直观易用的图形化平台。\n\n无论是希望快速上手的普通创作者、需要精细控制画面细节的设计师，还是想要深入探索模型潜力的开发者与研究人员，都能从中获益。其核心亮点在于极高的功能丰富度：不仅支持文生图、图生图、局部重绘（Inpainting）和外绘（Outpainting）等基础模式，还独创了注意力机制调整、提示词矩阵、负向提示词以及“高清修复”等高级功能。此外，它内置了 GFPGAN 和 CodeFormer 等人脸修复工具，支持多种神经网络放大算法，并允许用户通过插件系统无限扩展能力。即使是显存有限的设备，stable-diffusion-webui 也提供了相应的优化选项，让高质量的 AI 艺术创作变得触手可及。",162132,3,"2026-04-05T11:01:52",[13,14,15],"开发框架","图像","Agent","ready",{"id":18,"name":19,"github_repo":20,"description_zh":21,"stars":22,"difficulty_score":23,"last_commit_at":24,"category_tags":25,"status":16},1381,"everything-claude-code","affaan-m\u002Feverything-claude-code","everything-claude-code 是一套专为 AI 编程助手（如 Claude Code、Codex、Cursor 等）打造的高性能优化系统。它不仅仅是一组配置文件，而是一个经过长期实战打磨的完整框架，旨在解决 AI 代理在实际开发中面临的效率低下、记忆丢失、安全隐患及缺乏持续学习能力等核心痛点。\n\n通过引入技能模块化、直觉增强、记忆持久化机制以及内置的安全扫描功能，everything-claude-code 能显著提升 AI 在复杂任务中的表现，帮助开发者构建更稳定、更智能的生产级 AI 代理。其独特的“研究优先”开发理念和针对 Token 消耗的优化策略，使得模型响应更快、成本更低，同时有效防御潜在的攻击向量。\n\n这套工具特别适合软件开发者、AI 研究人员以及希望深度定制 AI 工作流的技术团队使用。无论您是在构建大型代码库，还是需要 AI 协助进行安全审计与自动化测试，everything-claude-code 都能提供强大的底层支持。作为一个曾荣获 Anthropic 黑客大奖的开源项目，它融合了多语言支持与丰富的实战钩子（hooks），让 AI 真正成长为懂上",140436,2,"2026-04-05T23:32:43",[13,15,26],"语言模型",{"id":28,"name":29,"github_repo":30,"description_zh":31,"stars":32,"difficulty_score":23,"last_commit_at":33,"category_tags":34,"status":16},2271,"ComfyUI","Comfy-Org\u002FComfyUI","ComfyUI 是一款功能强大且高度模块化的视觉 AI 引擎，专为设计和执行复杂的 Stable Diffusion 图像生成流程而打造。它摒弃了传统的代码编写模式，采用直观的节点式流程图界面，让用户通过连接不同的功能模块即可构建个性化的生成管线。\n\n这一设计巧妙解决了高级 AI 绘图工作流配置复杂、灵活性不足的痛点。用户无需具备编程背景，也能自由组合模型、调整参数并实时预览效果，轻松实现从基础文生图到多步骤高清修复等各类复杂任务。ComfyUI 拥有极佳的兼容性，不仅支持 Windows、macOS 和 Linux 全平台，还广泛适配 NVIDIA、AMD、Intel 及苹果 Silicon 等多种硬件架构，并率先支持 SDXL、Flux、SD3 等前沿模型。\n\n无论是希望深入探索算法潜力的研究人员和开发者，还是追求极致创作自由度的设计师与资深 AI 绘画爱好者，ComfyUI 都能提供强大的支持。其独特的模块化架构允许社区不断扩展新功能，使其成为当前最灵活、生态最丰富的开源扩散模型工具之一，帮助用户将创意高效转化为现实。",107662,"2026-04-03T11:11:01",[13,14,15],{"id":36,"name":37,"github_repo":38,"description_zh":39,"stars":40,"difficulty_score":23,"last_commit_at":41,"category_tags":42,"status":16},3704,"NextChat","ChatGPTNextWeb\u002FNextChat","NextChat 是一款轻量且极速的 AI 助手，旨在为用户提供流畅、跨平台的大模型交互体验。它完美解决了用户在多设备间切换时难以保持对话连续性，以及面对众多 AI 模型不知如何统一管理的痛点。无论是日常办公、学习辅助还是创意激发，NextChat 都能让用户随时随地通过网页、iOS、Android、Windows、MacOS 或 Linux 端无缝接入智能服务。\n\n这款工具非常适合普通用户、学生、职场人士以及需要私有化部署的企业团队使用。对于开发者而言，它也提供了便捷的自托管方案，支持一键部署到 Vercel 或 Zeabur 等平台。\n\nNextChat 的核心亮点在于其广泛的模型兼容性，原生支持 Claude、DeepSeek、GPT-4 及 Gemini Pro 等主流大模型，让用户在一个界面即可自由切换不同 AI 能力。此外，它还率先支持 MCP（Model Context Protocol）协议，增强了上下文处理能力。针对企业用户，NextChat 提供专业版解决方案，具备品牌定制、细粒度权限控制、内部知识库整合及安全审计等功能，满足公司对数据隐私和个性化管理的高标准要求。",87618,"2026-04-05T07:20:52",[13,26],{"id":44,"name":45,"github_repo":46,"description_zh":47,"stars":48,"difficulty_score":23,"last_commit_at":49,"category_tags":50,"status":16},2268,"ML-For-Beginners","microsoft\u002FML-For-Beginners","ML-For-Beginners 是由微软推出的一套系统化机器学习入门课程，旨在帮助零基础用户轻松掌握经典机器学习知识。这套课程将学习路径规划为 12 周，包含 26 节精炼课程和 52 道配套测验，内容涵盖从基础概念到实际应用的完整流程，有效解决了初学者面对庞大知识体系时无从下手、缺乏结构化指导的痛点。\n\n无论是希望转型的开发者、需要补充算法背景的研究人员，还是对人工智能充满好奇的普通爱好者，都能从中受益。课程不仅提供了清晰的理论讲解，还强调动手实践，让用户在循序渐进中建立扎实的技能基础。其独特的亮点在于强大的多语言支持，通过自动化机制提供了包括简体中文在内的 50 多种语言版本，极大地降低了全球不同背景用户的学习门槛。此外，项目采用开源协作模式，社区活跃且内容持续更新，确保学习者能获取前沿且准确的技术资讯。如果你正寻找一条清晰、友好且专业的机器学习入门之路，ML-For-Beginners 将是理想的起点。",84991,"2026-04-05T10:45:23",[14,51,52,53,15,54,26,13,55],"数据工具","视频","插件","其他","音频",{"id":57,"name":58,"github_repo":59,"description_zh":60,"stars":61,"difficulty_score":10,"last_commit_at":62,"category_tags":63,"status":16},3128,"ragflow","infiniflow\u002Fragflow","RAGFlow 是一款领先的开源检索增强生成（RAG）引擎，旨在为大语言模型构建更精准、可靠的上下文层。它巧妙地将前沿的 RAG 技术与智能体（Agent）能力相结合，不仅支持从各类文档中高效提取知识，还能让模型基于这些知识进行逻辑推理和任务执行。\n\n在大模型应用中，幻觉问题和知识滞后是常见痛点。RAGFlow 通过深度解析复杂文档结构（如表格、图表及混合排版），显著提升了信息检索的准确度，从而有效减少模型“胡编乱造”的现象，确保回答既有据可依又具备时效性。其内置的智能体机制更进一步，使系统不仅能回答问题，还能自主规划步骤解决复杂问题。\n\n这款工具特别适合开发者、企业技术团队以及 AI 研究人员使用。无论是希望快速搭建私有知识库问答系统，还是致力于探索大模型在垂直领域落地的创新者，都能从中受益。RAGFlow 提供了可视化的工作流编排界面和灵活的 API 接口，既降低了非算法背景用户的上手门槛，也满足了专业开发者对系统深度定制的需求。作为基于 Apache 2.0 协议开源的项目，它正成为连接通用大模型与行业专有知识之间的重要桥梁。",77062,"2026-04-04T04:44:48",[15,14,13,26,54],{"id":65,"github_repo":66,"name":67,"description_en":68,"description_zh":69,"ai_summary_zh":69,"readme_en":70,"readme_zh":71,"quickstart_zh":72,"use_case_zh":73,"hero_image_url":74,"owner_login":75,"owner_name":76,"owner_avatar_url":77,"owner_bio":78,"owner_company":79,"owner_location":80,"owner_email":81,"owner_twitter":82,"owner_website":83,"owner_url":84,"languages":85,"stars":90,"forks":91,"last_commit_at":92,"license":81,"difficulty_score":93,"env_os":94,"env_gpu":95,"env_ram":95,"env_deps":96,"category_tags":99,"github_topics":100,"view_count":23,"oss_zip_url":81,"oss_zip_packed_at":81,"status":16,"created_at":109,"updated_at":110,"faqs":111,"releases":112},3137,"ChandlerBang\u002Fawesome-self-supervised-gnn","awesome-self-supervised-gnn","Papers about pretraining and self-supervised learning on Graph Neural Networks (GNN).","awesome-self-supervised-gnn 是一个专注于图神经网络（GNN）自监督学习与预训练技术的学术资源合集。它系统性地整理了该领域的高质量研究论文，并按发表年份进行分类，旨在帮助从业者快速追踪从基础理论到前沿应用（如异常检测、推荐系统、谣言识别等）的最新进展。\n\n在图数据中，获取大量精准标注的成本极高，这往往限制了模型的性能上限。awesome-self-supervised-gnn 正是为了解决这一痛点而生，它汇聚了利用无标签数据进行高效表征学习的前沿方案，让模型能够在缺乏人工标注的情况下，依然挖掘出图结构中深层的结构与语义信息。\n\n这份资源特别适合人工智能研究人员、算法工程师以及对图学习感兴趣的高校师生使用。无论是希望开展新课题的学者，还是寻求技术落地的开发者，都能从中找到极具价值的参考。其独特亮点在于不仅提供论文链接，还尽可能附带了官方代码实现，并特别标记了高引用率的热门工作，极大地降低了复现经典算法和跟进技术热点的门槛，是探索图自监督学习领域不可或缺的导航图。","# awesome-self-supervised-gnn\n \n ![PRs Welcome](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FPRs-Welcome-green)  [![Awesome](https:\u002F\u002Fawesome.re\u002Fbadge.svg)](https:\u002F\u002Fawesome.re) ![Stars](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fstars\u002FChandlerBang\u002Fawesome-self-supervised-gnn?color=yellow)  ![Forks](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fforks\u002FChandlerBang\u002Fawesome-self-supervised-gnn?color=blue&label=Fork)\n \n This repository contains a list of papers on the **Self-supervised Learning on Graph Neural Networks (GNNs)**, we categorize them based on their published years.\n \n We will try to make this list updated. If you found any error or any missed paper, please don't hesitate to open issues or pull requests.\n \n Note: :fire: indicates the paper is extensively cited (e.g., > 80 citations). The code is provided in `get_hot.py`.\n\n## Year 2024\n1. [ICASSP 2024] **Contrastive Deep Nonnegative Matrix Factorization for Community Detection** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.02357) [[code]](https:\u002F\u002Fgithub.com\u002F6lyc\u002FCDNMF)\n   \n## Year 2023\n1. [ICLR 2023] **Empowering Graph Representation Learning with Test-Time Graph Transformation** [[paper]](https:\u002F\u002Fopenreview.net\u002Fforum?id=Lnxl5pr018) [[code]](https:\u002F\u002Fgithub.com\u002FChandlerBang\u002FGTrans)\n1. [ICLR 2023] **Multi-task Self-supervised Graph Neural Networks Enable Stronger Task Generalization** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2210.02016.pdf) [[code]](https:\u002F\u002Fgithub.com\u002Fjumxglhf\u002FParetoGNN)\n1. [AAAI 2023] **Eliciting Structural and Semantic Global Knowledge in Unsupervised Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.08480) [[code]](https:\u002F\u002Fgithub.com\u002Fkaize0409\u002FS-3-CL)\n1. [arXiv 2023] **Truncated Affinity Maximization: One-class Homophily Modeling for Graph Anomaly Detection** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2306.00006.pdf)\n1. [ICASSP 2023] **Contrastive Learning at the Relation and Event Level for Rumor Detection** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F10096567)\n1. [arXiv 2023] **AmGCL: Feature Imputation of Attribute Missing Graph via Self-supervised Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2305.03741.pdf)\n1. [arXiv 2023] **SEGA: Structural Entropy Guided Anchor View for Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2305.04501.pdf)\n1. [arXiv 2023] **CSGCL: Community-Strength-Enhanced Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2305.04658.pdf)\n1. [TKDE 2023] **MINING: Multi-Granularity Network Alignment Based on Contrastive Learning** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F10120956)\n1. [ICASSP 2023] **Select The Best: Enhancing Graph Representation with Adaptive Negative Sample Selection** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F10095586)\n1. [ICASSP 2023] **Graph Contrastive Learning with Learnable Graph Augmentation** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F10095511)\n1. [arXiv 2023] **FormNetV2: Multimodal Graph Contrastive Learning for Form Document Information Extraction** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2305.02549.pdf)\n1. [INS 2023] **A fairness-aware graph contrastive learning recommender framework for social tagging systems** [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0020025523006497)\n1. [arXiv 2023] **Improving Knowledge Graph Entity Alignment with Graph Augmentation** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2304.14585.pdf)\n1. [WWW 2023] **Graph Self-supervised Learning with Augmentation-aware Contrastive Learning** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3543507.3583246)\n1. [arXiv 2023] **A Systematic Survey of Chemical Pre-trained Models** [[paper]](https:\u002F\u002Fsxkdz.github.io\u002Ffiles\u002Fpublications\u002FIJCAI\u002FCPM\u002FCPM.pdf)\n1. [WWW 2023] **Self-Supervised Teaching and Learning of Representations on Graphs** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3543507.3583441)\n1. [TKDE 2023] **Progressive Hard Negative Masking: From Global Uniformity to Local Tolerance** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F10111083)\n1. [KBS 2023] **ST-A-PGCL: Spatiotemporal adaptive periodical graph contrastive learning for traffic prediction under real scenarios** [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0950705123003416)\n1. [WWW 2023] **SeeGera: Self-supervised Semi-implicit Graph Variational Auto-encoders with Masking** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3543507.3583245)\n1. [INS 2023] **Self-supervised Contrastive Learning on Heterogeneous Graphs with Mutual Constraints of Structure and Feature** [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0020025523006114)\n1. [Scientific Reports 2023] **A multi-view contrastive learning for heterogeneous network embedding** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-023-33324-7)\n1. [WWW 2023] **Automated Spatio-Temporal Graph Contrastive Learning** [[paper]](https:\u002F\u002Fzhengwang125.github.io\u002Fpaper\u002FSTGCL_WWW23.pdf)\n1. [arXiv 2023] **Capturing Fine-grained Semantics in Contrastive Graph Representation Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2304.11658.pdf)\n1. [arXiv 2023] **Decouple Graph Neural Networks: Train Multiple Simple GNNs Simultaneously Instead of One** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2304.10126.pdf)\n1. [arXiv 2023] **ID-MixGCL: Identity Mixup for Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2304.10045.pdf)\n1. [Bioinformatics 2023] **Molecular Property Prediction by Contrastive Learning with Attention-Guided Positive Sample Selection** [[paper]](https:\u002F\u002Fwatermark.silverchair.com\u002Fbtad258.pdf?token=AQECAHi208BE49Ooan9kkhW_Ercy7Dm3ZL_9Cf3qfKAc485ysgAAAwcwggMDBgkqhkiG9w0BBwagggL0MIIC8AIBADCCAukGCSqGSIb3DQEHATAeBglghkgBZQMEAS4wEQQMdupp3nabpyWrY1TvAgEQgIICuslU3gktfD9EQ9YOuajKd5nL5RNR0eI5eAOngtpUfOUcqcGOQONeb7Lznmgz8twSmMS13_U5bKR6FRKpce_1s9teGI5K7J6JLdx_sHrlBZGP8m1xMzk7soYc8pGHVsgbKwusPR5rkaRd-JykOSM3eIn_5IQgqqJ2RYmtcymvywcuGV1tA__M44XepfMuzHcC9q5h8NuWaWXmMzode9nlFyO0eacGBbSG8zvaH97K65aD734tbaUW60Do6fS_5yq9kRMFV3EPqnJwJ0iJ72o3ZFSNBjxb2yDH1kd_TZbkmio6LC6ZH8mrubOKxGDhrzjruSEpe1Fs54BzZfrqrGbmv8LB9sWxbSXAitKbMGnFb1WxyBF6cyB9g1AyqGYJEMr7HM7yBC9UOmff_s1kH-Avd_L8ZfzyhVqDvUyIgJc39Nlw6Eju3stlDuKMIwwWBI6qWHkc_nEd_0u7n1ssxbBydo63PZKmNbtsq36l7wN0goc_sWYXy9AyMu0ROFNLfWSe6n6k_u7DIyRlm7GPzOrx3CEaCWq_8uw1Pkvygflhz4aktGzWUBxodPezX4ToO2_9Q7IP9IjccsCI_zcr38C3EaHhtZf4yXFCowrL7C7MOLq9yo_9huTv3UJ_qq0dL7UCnJgrkI0kK7pkljnSu2gd0iuxwftCnphrXiw79xJwVUXTvbWKe_xxoh_XHllwhztCmPFYFbmwB-1A2gYpWq2fnNl7LxxvnioJCuoz9mwaFXN6tLwCCPkZa-GdakTaoHoU30JGMvrgdyhhFU30mUN5NOyWaoOLcqFLy8y-mO_V07uUGmMkS3SHM0j-qYEdjVEddM7QxbW5JW28EkL3L97BWaBohCHcj0jiS7pzteOwzZ4e3WWhghFX1pDGeFvvhzv5xCobn5TPFV1N9qk7I7QrEZSjAg1epeLNvohj)\n1. [AISTAT 2023] **Learning Robust Graph Neural Networks with Limited Supervision** [[paper]](https:\u002F\u002Fproceedings.mlr.press\u002Fv206\u002Falchihabi23a\u002Falchihabi23a.pdf)\n1. [TNNLS 2023] **Demystifying and Mitigating Bias for Node Representation Learning** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F10103678)\n1. [BICTA 2023] **Graph Contrastive Learning with Intrinsic Augmentations** [[paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-981-99-1549-1_27)\n1. [arXiv 2023] **GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learner** [[paper]](https:\u002F\u002Fepubs.siam.org\u002Fdoi\u002Fpdf\u002F10.1137\u002F1.9781611977653.ch19)\n1. [arXiv 2023] **Adversarial Hard Negative Generation for Complementary Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2304.04779.pdf)\n1. [INS 2023] **INS-GNN: Improving Graph Imbalance Learning with Self-Supervision** [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0020025523005042)\n1. [TNNLS 2023] **Dual Contrastive Learning Network for Graph Clustering** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F10097557)\n1. [arXiv 2023] **RARE: Robust Masked Graph Autoencoder** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2304.01507.pdf)\n1. [TKDE 2023] **Maximizing Mutual Information Across Feature and Topology Views for Representing Graphs** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F10093032)\n1. [arXiv 2023] **When to Pre-Train Graph Neural Networks? An Answer from Data Generation Perspective!** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.16458)\n1. [KBS 2023] **Class-homophilic-based data augmentation for improving graph neural networks** [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS095070512300268X)\n1. [arXiv 2023] **Structural Imbalance Aware Graph Augmentation Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2303.13757.pdf)\n1. [arXiv 2023] **Hybrid Augmented Automated Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2303.15182.pdf)\n1. [arXiv 2023] **Decoupling Graph Neural Network with Contrastive Learning for Fraud Detection** [[paper]](https:\u002F\u002Flinmengsysu.github.io\u002Fslides\u002Fmain.pdf)\n1. [arXiv 2023] **Data-Centric Learning from Unlabeled Graphs with Diffusion Model** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2303.10108.pdf)\n1. [TPAMI 2023] **Unsupervised Learning of Graph Matching With Mixture of Modes Via Discrepancy Minimization** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F10073537)\n1. [arXiv 2023] **NESS: Learning Node Embeddings from Static SubGraphs** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2303.08958.pdf)\n1. [Sensors 2023] **A Robust Automated Analog Circuits Classification Involving a Graph Neural Network and a Novel Data Augmentation Strategy** [[paper]](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F23\u002F6\u002F2989)\n1. [arXiv 2023] **Contrastive knowledge integrated graph neural networks for Chinese medical text classification** [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0952197623002415)\n1. [arXiv 2023] **CHGNN: A Semi-Supervised Contrastive Hypergraph Learning Network** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2303.06213.pdf)\n1. [arXiv 2023] **Contrastive Learning under Heterophily** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2303.06344.pdf)\n1. [arXiv 2023] **Structure-Aware Group Discrimination with Adaptive-View Graph Encoder: A Fast Graph Contrastive Learning Framework** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2303.05231.pdf)\n1. [TNNLS 2023] **Self-supervised Learning IoT Device Features with Graph Contrastive Neural Network for Device Classification in Social Internet of Things** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F10059194)\n1. [TKDE 2023] **Feature-Level Deeper Self-Attention Network With Contrastive Learning for Sequential Recommendation** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F10059216)\n1. [AAAI 2023] **Recommend What to Cache: a Simple Self-supervised Graph-based Recommendation Framework for Edge Caching Network** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2302.14438.pdf)\n1. [arXiv 2023] **Self-Supervised Interest Transfer Network via Prototypical Contrastive Learning for Recommendation** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2302.14438.pdf)\n1. [arXiv 2023] **SGL-PT: A Strong Graph Learner with Graph Prompt Tuning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2302.12449.pdf)\n1. [CIS 2023] **SimGRL: a simple self-supervised graph representation learning framework via triplets** [[paper]](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs40747-023-00997-6)\n1. [WSDM 2023] **Self-Supervised Group Graph Collaborative Filtering for Group Recommendation** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3539597.3570400)\n1. [WSDM 2023] **S2GAE: Self-Supervised Graph Autoencoders are Generalizable Learners with Graph Masking** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3539597.3570404)\n1. [WSDM 2023] **Heterogeneous Graph Contrastive Learning for Recommendation** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3539597.3570484)\n1. [Nature Communications Chemistry] **Hierarchical Molecular Graph Self-Supervised Learning for property prediction** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs42004-023-00825-5)\n1. [arXiv 2023] **Wiener Graph Deconvolutional Network Improves Graph Self-Supervised Learning** [[paper]](https:\u002F\u002Fwww.researchgate.net\u002Fprofile\u002FJia-Li-127\u002Fpublication\u002F368543822_Wiener_Graph_Deconvolutional_Network_Improves_Graph_Self-Supervised_Learning\u002Flinks\u002F63edebc419130a1a4a830593\u002FWiener-Graph-Deconvolutional-Network-Improves-Graph-Self-Supervised-Learning.pdf)\n1. [arXiv 2023] **Heterogeneous Social Event Detection via Hyperbolic Graph Representations** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2302.10362.pdf)\n1. [arXiv 2023] **LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2302.08191.pdf)\n1. [arXiv 2023] **GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2302.08043.pdf)\n1. [Pattern Recognition] **Dual-Channel Graph Contrastive Learning for Self-Supervised Graph-Level Representation Learning** [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0031320323001486)\n1. [NCA 2023] **Self-supervised contrastive learning for heterogeneous graph based on multi-pretext tasks** [[paper]](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs00521-023-08234-4)\n1. [arXiv 2023] **STERLING: Synergistic Representation Learning on Bipartite Graphs** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2302.05428.pdf)\n 1. [ICLR 2023] **Multi-task Self-supervised Graph Neural Networks Enable Stronger Task Generalization** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2210.02016.pdf)\n1. [WBD 2023] **Mixed-Order Heterogeneous Graph Pre-training for Cold-Start Recommendation** [[paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-25201-3_14)\n1. [arXiv 2023] **Explainable Action Prediction through Self-Supervision on Scene Graphs** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2302.03477.pdf)\n1. [arXiv 2023] **Spectral Augmentations for Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2302.02909.pdf)\n1. [RS 2023] **Representing Spatial Data with Graph Contrastive Learning** [[paper]](https:\u002F\u002Furldefense.com\u002Fv3\u002F__https:\u002F\u002Fscholar.google.com\u002Fscholar_url?url=https:**Awww.mdpi.com*2072-4292*15*4*880*pdf&hl=en&sa=X&d=18081949848644790374&ei=UtHkY-wUjdbJBK-AnIgN&scisig=AAGBfm2HRbUL2s5kW_fO96HIgBt-0lesJg&oi=scholaralrt&hist=Pv-V2igAAAAJ:16610178827432183357:AAGBfm3PSUTRAat5lSIOYWJJQSKiKvjk4g&html=&pos=1&folt=cit__;Ly8vLy8vLw!!KwNVnqRv!DcYtDY-xLzHkhx2yQ32kw_CetJ1VrPiy0H9Hilie6oEU0a9OMbDAWoV9kq6mhcDPope5FTQwyDvFJ1YT8B6R9su2t7P1Rg$)\n1. [ACLF 2023] **KE-GCL: Knowledge Enhanced Graph Contrastive Learning for Commonsense Question Answering** [[paper]](https:\u002F\u002Faclanthology.org\u002F2022.findings-emnlp.6.pdf)\n1. [TNNLS 2023] **GRLC: Graph Representation Learning With Constraints** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F10036344)\n1. [ESA 2023] **Contrastive graph clustering with adaptive filter** [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS095741742300146X)\n1. [arXiv 2023] **Biomedical Interaction Prediction with Adaptive Line Graph Contrastive Learning** [[paper]](https:\u002F\u002Fwww.mdpi.com\u002F2227-7390\u002F11\u002F3\u002F732)\n1. [arXiv 2023] **Affinity Uncertainty-based Hard Negative Mining in Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2301.13340.pdf)\n1. [arXiv 2023] **Self-supervised Semi-implicit Graph Variational Auto-encoders with Masking** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2301.12458.pdf)\n1. [ACM Trans. Web 2023] **Contrastive Graph Similarity Networks** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fpdf\u002F10.1145\u002F3580511)\n1. [ICBD 2023] **Predictive Masking for Semi-Supervised Graph Contrastive Learning** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F10020970)\n1. [TNNLS 2023] **Graph Representation Learning With Adaptive Metric** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F10025823)\n1. [RAL 2023] **Self-Supervised Local Topology Representation for Random Cluster Matching** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F10021967)\n1. [KBS 2023] **CrysGNN: Distilling pre-trained knowledge to enhance property prediction for crystalline materials** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2301.05852.pdf)\n1. [Entropy 2023] **A Semantic-Enhancement-Based Social Network User-Alignment Algorithm** [[paper]](https:\u002F\u002Fwww.mdpi.com\u002F1099-4300\u002F25\u002F1\u002F172)\n1. [KBS 2023] **Cross-view temporal graph contrastive learning for session-based recommendation** [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0950705123000540)\n1. [PR 2023] **Robust Image Clustering via Context-aware Contrastive Graph Learning** [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0031320323000419)\n1. [ICMLCS 2023] **AP-GCL: Adversarial Perturbation on Graph Contrastive Learning** [[paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-20096-0_47)\n1. [arXiv 2023] **Signed Directed Graph Contrastive Learning with Laplacian Augmentation** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2301.05163.pdf)\n1. [OJCS 2023] **SC-FGCL: Self-adaptive Cluster-based Federal Graph Contrastive Learning** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?arnumber=10015148)\n1. [BIB 2023] **CasANGCL: pre-training and fine-tuning model based on cascaded attention network and graph contrastive learning for molecular property prediction** [[paper]](https:\u002F\u002Facademic.oup.com\u002Fbib\u002Fadvance-article\u002Fdoi\u002F10.1093\u002Fbib\u002Fbbac566\u002F6966532)\n1. [AAAI 2023] **Spectral Feature Augmentation for Graph Contrastive Learning and Beyond** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2212.01026)\n1. [Entropy 2023] **Self-Supervised Node Classification with Strategy and Actively Selected Labeled Set** [[paper]](https:\u002F\u002Furldefense.com\u002Fv3\u002F__https:\u002F\u002Fscholar.google.com\u002Fscholar_url?url=https:**Awww.mdpi.com*1099-4300*25*1*30*pdf&hl=en&sa=X&d=13649462741514245070&ei=66yqY9q-NY_mmgHdka7oCw&scisig=AAGBfm0m2E6wg_90swKhBWYDrZsXMBr2kA&oi=scholaralrt&hist=Pv-V2igAAAAJ:16610178827432183357:AAGBfm3PSUTRAat5lSIOYWJJQSKiKvjk4g&html=&pos=0&folt=cit__;Ly8vLy8vLw!!KwNVnqRv!FbRTWxTuNHDzvvuiJFFzysRQQ3C08EMs3qJTdLHxTA4E2WK7FjMv32fbi6T1irhYspBlmsafx0xexY4FKuao4dHXv3q7hw$)\n \n ## Year 2022\n 1. [NeurIPS 2022] **Generalized Laplacian Eigenmaps** [[paper]](https:\u002F\u002Fopenreview.net\u002Fforum?id=HjicdpP-Nth)\n 1. [KDD 2022] **COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive Learning** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3534678.3539425)\n 1. [ITBE 2022] **Contrastive Multi-view Composite Graph Convolutional Networks Based on Contribution Learning for Autism Spectrum Disorder Classification** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9999336)\n 1. [IEEE Access 2022] **ROME: A Graph Contrastive Multi-view Framework from Hyperbolic Angular Space for MOOCs Recommendation** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?arnumber=10001755)\n 1. [arXiv 2022] **Heterogeneous Graph Contrastive Learning with Meta-path Contexts and Weighted Negative Samples** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2212.13847.pdf)\n 1. [arXiv 2022] **MolCPT: Molecule Continuous Prompt Tuning to Generalize Molecular Representation Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2212.10614.pdf)\n 1. [arXiv 2022] **Toward Improved Generalization: Meta Transfer of Self-supervised Knowledge on Graphs** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2212.08217.pdf)\n 1. [arXiv 2022] **Coarse-to-Fine Contrastive Learning on Graphs** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2212.06423.pdf)\n 1. [arXiv 2022] **MA-GCL: Model Augmentation Tricks for Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2212.07035.pdf)\n 1. [arXiv 2022] **Mul-GAD: a semi-supervised graph anomaly detection framework via aggregating multi-view information** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2212.05478.pdf)\n 1. [arXiv 2022] **Localized Contrastive Learning on Graphs** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2212.04604.pdf)\n 1. [arXiv 2022] **Alleviating neighbor bias: augmenting graph self-supervise learning with structural equivalent positive samples** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2212.04365.pdf)\n 1. [arXiv 2022] **Self-supervised Graph Representation Learning for Black Market Account Detection** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2212.02679.pdf)\n 1. [arXiv 2022] **Contrastive Deep Graph Clustering with Learnable Augmentation** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2212.03559.pdf)\n 1. [arXiv 2022] **Graph Anomaly Detection via Multi-Scale Contrastive Learning Networks with Augmented View** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2212.00535.pdf)\n 1. [arXiv 2022] **Self Supervised Clustering of Traffic Scenes using Graph Representations** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2211.15508.pdf)\n 1. [arXiv 2022] **Graph Contrastive Learning for Materials** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2211.13408.pdf)\n 1. [arXiv 2022] **Link Prediction with Non-Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2211.14394.pdf)\n 1. [IJMIR 2022] **TCKGE: Transformers with contrastive learning for knowledge graph embedding** [[paper]](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs13735-022-00256-3)\n 1. [arXiv 2022] **Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily Discriminating** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2211.14065.pdf)\n 1. [Neural Networks 2022] **Unsupervised graph-level representation learning with hierarchical contrasts** [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0893608022004609)\n 1. [arXiv 2022] **Relation-dependent Contrastive Learning with Cluster Sampling for Inductive Relation Prediction** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2211.12266.pdf)\n 1. [arXiv 2022] **Relational Symmetry based Knowledge Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2211.10738.pdf)\n 1. [arXiv 2022] **Towards Generalizable Graph Contrastive Learning: An Information Theory Perspective** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2211.10929.pdf)\n 1. [arXiv 2022] **Can Single-Pass Contrastive Learning Work for Both Homophilic and Heterophilic Graph?** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2211.10890.pdf)\n 1. [SIGSPATIAL 2022] **When Do Contrastive Learning Signals Help Spatio-Temporal Graph Forecasting?** [[paper]](http:\u002F\u002Furban-computing.com\u002Fpdf\u002FSTGCL_SIGSPATIAL_22.pdf)\n 1. [Scientific Reports 2022] **Deep graph level anomaly detection with contrastive learning** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-022-22086-3)\n 1. [TII 2022] **Semi-supervised machine fault diagnosis fusing unsupervised graph contrastive learning** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9944187)\n 1. [KBS 2022] **SMGCL: Semi-supervised Multi-view Graph Contrastive Learning** [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0950705122012163)\n 1. [arXiv 2022] **Unsupervised Graph Contrastive Learning with Data Augmentation for Malware Classification** [[paper]](https:\u002F\u002Fwww.researchgate.net\u002Fprofile\u002FYun-Gao-48\u002Fpublication\u002F365275847_Unsupervised_Graph_Contrastive_Learning_with_Data_Augmentation_for_Malware_Classification\u002Flinks\u002F636cec632f4bca7fd04b9a26\u002FUnsupervised-Graph-Contrastive-Learning-with-Data-Augmentation-for-Malware-Classification.pdf)\n 1. [IJCRS 2022] **Multi-scale Subgraph Contrastive Learning for Link Prediction** [[paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-21244-4_16)\n 1. [arXiv 2022] **Flaky Performances when Pretraining on Relational Databases** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2211.05213.pdf)\n 1. [arXiv 2022] **GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2211.04208.pdf)\n 1. [ATKDD 2022] **Ada-MIP: Adaptive Self-supervised Graph Representation Learning via Mutual Information and Proximity Optimization** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fpdf\u002F10.1145\u002F3568165)\n 1. [arXiv 2022] **Graph Contrastive Learning with Implicit Augmentations** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2211.03710.pdf)\n 1. [Information Sciences 2022] **Contrastive Graph Neural Network-based Camouflaged Fraud Detector** [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0020025522011926)\n 1. [arXiv 2022] **DyG2Vec: Representation Learning for Dynamic Graphs with Self-Supervision** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2210.16906.pdf)\n 1. [arXiv 2022] **Federated Graph Representation Learning using Self-Supervision** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2210.15120.pdf)\n 1. [arXiv 2022] **Benchmark of Self-supervised Graph Neural Networks** [[paper]](https:\u002F\u002Faaltodoc.aalto.fi\u002Fbitstream\u002Fhandle\u002F123456789\u002F116441\u002Fmaster_Wang_Haishan_2022.pdf?sequence=2)\n 1. [arXiv 2022] **Line Graph Contrastive Learning for Link Prediction** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2210.13795.pdf)\n 1. [TDSC 2022] **FewM-HGCL: Few-Shot Malware Variants Detection Via Heterogeneous Graph Contrastive Learning** [[paper]](https:\u002F\u002Fwww.computer.org\u002Fcsdl\u002Fjournal\u002Ftq\u002F5555\u002F01\u002F09928211\u002F1HJuUzzFey4)\n 1. [arXiv 2022] **Self-supervised Graph-based Point-of-interest Recommendation** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2210.12506.pdf)\n 1. [IJMLC 2022] **Hybrid sampling-based contrastive learning for imbalanced node classification** [[paper]](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs13042-022-01677-6)\n 1. [CIKM 2022] **Temporality-and Frequency-aware Graph Contrastive Learning for Temporal Network** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3511808.3557469)\n 1. [CIKM 2022] **Towards Self-supervised Learning on Graphs with Heterophily** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3511808.3557478)\n 1. [ISWC 2022] **HCL: Improving Graph Representation with Hierarchical Contrastive Learning** [[paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-19433-7_7)\n 1. [CIKM 2022] **Cognize Yourself: Graph Pre-Training via Core Graph Cognizing and Differentiating** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3511808.3557259)\n 1. [CIKM 2022] **AdaGCL: Adaptive Subgraph Contrastive Learning to Generalize Large-scale Graph Training** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3511808.3557228)\n 1. [CIKM 2022] **Look Twice as Much as You Say: Scene Graph Contrastive Learning for Self-Supervised Image Caption Generation** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3511808.3557382)\n 1. [CIKM 2022] **Malicious Repositories Detection with Adversarial Heterogeneous Graph Contrastive Learning** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3511808.3557384)\n 1. [ICEBE 2022] **Self-supervised Heterogeneous Graph Pre-training Based on Structural Clustering** [[paper]](https:\u002F\u002Fconferences.computer.org\u002Ficebe\u002F2022\u002Ficebe2022-proceedings\u002FKnowledge%20Graph%20Completion%20based%20on%20Hyperbolic%20Graph%20Contrastive%20Attention%20Network.pdf)\n 1. [arXiv 2022] **Self-supervised Heterogeneous Graph Pre-training Based on Structural Clustering** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2210.10462.pdf)\n 1. [NeurIPS 2022] **Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.03801) [[code]](https:\u002F\u002Fgithub.com\u002Fweitianxin\u002FHyperGCL)\n 1. [ICCL 2022] **Modeling Intra-and Inter-Modal Relations: Hierarchical Graph Contrastive Learning for Multimodal Sentiment Analysis** [[paper]](https:\u002F\u002Faclanthology.org\u002F2022.coling-1.622.pdf)\n 1. [TKDE 2022] **Adversarial Contrastive Learning for Evidence-aware Fake News Detection with Graph Neural Networks** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2210.05498.pdf)\n 1. [MM 2022] **Simple Self-supervised Multiplex Graph Representation Learning** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3503161.3547949)\n 1. [TMM 2022] **Self-consistent Contrastive Attributed Graph Clustering with Pseudo-label Prompt** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9914670)\n 1. [NeurIPS 2022] **Uncovering the Structural Fairness in Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2210.03011.pdf)\n 1. [NeurIPS 2022] **Revisiting Graph Contrastive Learning from the Perspective of Graph Spectrum** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2210.02330.pdf)\n 1. [arXiv 2022] **Heterogeneous Graph Contrastive Multi-view Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2210.00248.pdf)\n 1. [arXiv 2022] **Automated Graph Self-supervised Learning via Multi-teacher Knowledge Distillation** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2210.02099.pdf)\n 1. [arXiv 2022] **Prompt Tuning for Graph Neural Networks** [[paper]](https:\u002F\u002Fweb10.arxiv.org\u002Fpdf\u002F2209.15240.pdf)\n 1. [arXiv 2022] **Improving Molecular Pretraining with Complementary Featurizations** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2209.15101.pdf)\n 1. [arXiv 2022] **Graph Soft-Contrastive Learning via Neighborhood Ranking** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2209.13964.pdf)\n 1. [EDBT 2022] **Spatial Structure-Aware Road Network Embedding via Graph Contrastive Learning** [[paper]](https:\u002F\u002Fopenproceedings.org\u002F2023\u002Fconf\u002Fedbt\u002Fpaper-193.pdf)\n 1. [arXiv 2022] **Adversarial Cross-View Disentangled Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2209.07699.pdf)\n 1. [Neurocomputing 2022] **Motifs-based Recommender System via Hypergraph Convolution and Contrastive Learning** [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0925231222011948)\n 1. [TNNLS 2022] **Graph Representation Learning for Large-Scale Neuronal Morphological Analysis** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9895206)\n 1. [ECML-PKDD 2022] **Self-supervised Graph Learning with Segmented Graph Channels** [[paper]](https:\u002F\u002F2022.ecmlpkdd.org\u002Fwp-content\u002Fuploads\u002F2022\u002F09\u002Fsub_216.pdf)\n 1. [ECML-PKDD 2022] **Graph Contrastive Learning with Adaptive Augmentation for Recommendation** [[paper]](https:\u002F\u002F2022.ecmlpkdd.org\u002Fwp-content\u002Fuploads\u002F2022\u002F09\u002Fsub_650.pdf)\n 1. [CIKM 2022] **Contrastive Knowledge Graph Error Detection** [[paper]](https:\u002F\u002Fwww4.comp.polyu.edu.hk\u002F~xiaohuang\u002Fdocs\u002FQinggang_CIKM2022.pdf)\n 1. [TKDE 2022] **Disentangled Graph Contrastive Learning With Independence Promotion** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9893319)\n 1. [ECML-PKDD 2022] **Supervised Graph Contrastive Learning for Few-shot Node Classification** [[paper]](https:\u002F\u002F2022.ecmlpkdd.org\u002Fwp-content\u002Fuploads\u002F2022\u002F09\u002Fsub_764.pdf)\n 1. [Information Sciences 2022] **Graph Prototypical Contrastive Learning** [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS002002552201057X)\n 1. [ICAAN 2022] **Knowledge-Aware Self-supervised Graph Representation Learning for Recommendation** [[paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-15937-4_35)\n 1. [arXiv 2022] **Self-supervised Representation Learning on Electronic Health Records with Graph Kernel Infomax** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2209.00655.pdf)\n 1. [arXiv 2022] **Disentangled Graph Contrastive Learning for Review-based Recommendation** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2209.01524.pdf)\n 1. [arXiv 2022] **Contrastive Learning with Heterogeneous Graph Attention Networks on Short Text Classification** [[paper]](https:\u002F\u002Fdro.dur.ac.uk\u002F36856\u002F1\u002F36856.pdf)\n 1. [arXiv 2022] **Features Based Adaptive Augmentation for Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fftp\u002Farxiv\u002Fpapers\u002F2207\u002F2207.01792.pdf)\n 1. [TKDE 2022] **GCCAD: Graph Contrastive Learning for Anomaly Detection** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9870034)\n 1. [JCIM 2022] **SMICLR: Contrastive Learning on Multiple Molecular Representations for Semisupervised and Unsupervised Representation Learning** [[paper]](https:\u002F\u002Fpubs.acs.org\u002Fdoi\u002Ffull\u002F10.1021\u002Facs.jcim.2c00521)\n 1. [arXiv 2022] **XSimGCL: Towards Extremely Simple Graph Contrastive Learning for Recommendation** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.02544)[[code]](https:\u002F\u002Fgithub.com\u002FCoder-Yu\u002FSELFRec)\n 1. [CIKM 2022] **Relational Self-Supervised Learning on Graphs** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2208.10493.pdf)[[code]](https:\u002F\u002Fgithub.com\u002FNamkyeong\u002FRGRL)\n 1. [Information Sciences 2022] **Self-Supervised Graph Representation Learning via Positive Mining** [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0020025522009495)\n 1. [arXiv 2022] **Heterogeneous Graph Masked Autoencoders** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2208.09957.pdf)\n 1. [arXiv 2022] **KRACL: Contrastive Learning with Graph Context Modeling for Sparse Knowledge Graph Completion** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2208.07622.pdf)\n 1. [arXiv 2022] **R\\'enyiCL: Contrastive Representation Learning with Skew R\\'enyi Divergence** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2208.06270.pdf)\n 1. [TNNLS 2022] **Prototypical Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2106.09645.pdf)\n 1. [KDD 2022] **Mining Spatio-Temporal Relations via Self-Paced Graph Contrastive Learning** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3534678.3539422)\n 1. [KDD 2022] **Rep2Vec: Repository Embedding via Heterogeneous Graph Adversarial Contrastive Learning** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3534678.3539324)\n 1. [arXiv 2022] **Deep Contrastive Multiview Network Embedding** [[paper]](https:\u002F\u002Fsxkdz.github.io\u002Ffiles\u002Fpublications\u002FCIKM\u002FCREME\u002FCREME.pdf)\n 1. [arXiv 2022] **Analyzing Data-Centric Properties for Contrastive Learning on Graphs** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2208.02810.pdf)\n 1. [KDD 2022] **Mask and Reason: Pre-Training Knowledge Graph Transformers for Complex Logical Queries** [[paper]](https:\u002F\u002Fkeg.cs.tsinghua.edu.cn\u002Fjietang\u002Fpublications\u002FKDD22-Liu-et-al-KG-Transformer.pdf)\n 1. [arXiv 2022] **Generative Subgraph Contrast for Self-Supervised Graph Representation Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2207.11996.pdf)\n 1. [IJCAI 2022] **Graph Masked Autoencoder Enhanced Predictor for Neural Architecture Search** [[paper]](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F0432.pdf)\n 1. [IJCAI 2022] **Proximity Enhanced Graph Neural Networks with Channel Contrast** [[paper]](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F0340.pdf)\n 1. [IJCAI 2022] **Rethinking the Promotion Brought by Contrastive Learning to Semi-Supervised Node Classification** [[paper]](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F0395.pdf)\n 1. [IPM 2022] **HCNA: Hyperbolic Contrastive Learning Framework for Self-Supervised Network Alignment** [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS0306457322001315)\n 1. [arXiv 2022] **3D Equivariant Molecular Graph Pretraining** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2207.08824.pdf)\n 1. [arXiv 2022] **Unified 2D and 3D Pre-Training of Molecular Representations** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2207.08806.pdf)\n 1. [arXiv 2022] **Does GNN Pretraining Help Molecular Representation?** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2207.06010.pdf)\n 1. [arXiv 2022] **Latent Augmentation For Better Graph Self-Supervised Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2206.12933.pdf)\n 1. [arXiv 2022] **Geometry Contrastive Learning on Heterogeneous Graphs** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2206.12547.pdf)\n 1. [KIS 2022] **Self-supervised role learning for graph neural networks** [[paper]](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10115-022-01694-5)\n 1. [JFCST 2022] **Graph Neural Network Defense Combined with Contrastive Learning** [[paper]](http:\u002F\u002Ffcst.ceaj.org\u002FEN\u002Farticle\u002FdownloadArticleFile.do?attachType=PDF&id=3113)\n 1. [ICMLW 2022] **Evaluating Self-Supervised Learned Molecular Graphs** [[paper]](https:\u002F\u002Fopenreview.net\u002Fpdf?id=LeJC_Mf5rx-)\n 1. [KDD 2022] **Reliable Representations Make A Stronger Defender: Unsupervised Structure Refinement for Robust GNN** [[paper]](https:\u002F\u002Fponderly.github.io\u002Fpub\u002FSTABLE_KDD2022.pdf)\n 1. [ICMLW 2022] **Featurizations Matter: A Multiview Contrastive Learning Approach to Molecular Pretraining** [[paper]](https:\u002F\u002Fopenreview.net\u002Fpdf?id=Pm1Q1X3avx1)\n 1. [bioRiv 2022] **Cross-modal Graph Contrastive Learning with Cellular Images** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002Fbiorxiv\u002Fearly\u002F2022\u002F06\u002F06\u002F2022.06.05.494905.full.pdf)\n 1. [Information Sciences 2022] **A new self-supervised task on graphs: Geodesic distance prediction** [[paper]]([https:\u002F\u002Fhansen7.github.io\u002Fsandbox\u002Fmolgrapheval.pdf](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS0020025522006375))\n 1. [arXiv 2022] **Evaluating Self-Supervised Learning for Molecular Graph Embeddings** [[paper]](https:\u002F\u002Fhansen7.github.io\u002Fsandbox\u002Fmolgrapheval.pdf)\n 1. [arXiv 2022] **Evaluating Graph Generative Models with Contrastively Learned Features** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2206.06234.pdf)\n 1. [arXiv 2022] **COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2206.04726.pdf)\n 1. [arXiv 2022] **Decoupled Self-supervised Learning for Non-Homophilous Graphs** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2206.03601.pdf)\n 1. [arXiv 2022] **Interpolation-based Correlation Reduction Network for Semi-Supervised Graph Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2206.02796.pdf)\n 1. [arXiv 2022] **Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2206.01535.pdf)\n 1. [arXiv 2022] **KPGT: Knowledge-Guided Pre-training of Graph Transformer for Molecular Property Prediction** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2206.03364.pdf)\n 1. [CVPR 2022] **Robust Optimization As Data Augmentation for Large-Scale Graphs** [[paper]](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fpapers\u002FKong_Robust_Optimization_As_Data_Augmentation_for_Large-Scale_Graphs_CVPR_2022_paper.pdf)\n 1. [arXiv 2022] **COIN: Co-Cluster Infomax for Bipartite Graphs** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2206.00006.pdf)\n 3. [TSIPN 2022] **Fair Contrastive Learning on Graphs** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9779533)\n 4. [arXiv 2022] **I’m Me, We’re Us, and I’m Us: Tri-directional Contrastive Learning on Hypergraphs** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2206.04739.pdf)\n 5. [TNNLS 2022] **CLEAR: Cluster-Enhanced Contrast for Self-Supervised Graph Representation Learning** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9791433)\n 6. [arXiv 2022] **Let Invariant Rationale Discovery Inspire Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2206.07869.pdf)\n 7. [arXiv 2022] **Omni-Granular Ego-Semantic Propagation for Self-Supervised Graph Representation Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2205.15746.pdf)\n 8. [arXiv 2022] **Improving Subgraph Representation Learning via Multi-View Augmentation** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2205.13038.pdf)\n 9. [arXiv 2022] **Triangular Contrastive Learning on Molecular Graphs** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2205.13279.pdf)\n 10. [KDD 2022] **GraphMAE: Self-supervised Masked Graph Autoencoders** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2205.10803.pdf)\n 11. [arXiv 2022] **MaskGAE: Masked Graph Modeling Meets Graph Autoencoders** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2205.10053.pdf)\n 12. [ICML 2022] **Understanding Limitations of Unsupervised Graph Representation Learning from a Data-Dependent Perspective** [[paper]](https:\u002F\u002Fwww.osti.gov\u002Fservlets\u002Fpurl\u002F1868861)\n 13. [arXiv 2022] **Towards Explanation for Unsupervised Graph-Level Representation Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2205.09934.pdf)\n 14. [arXiv 2022] **ImGCL: Revisiting Graph Contrastive Learning on Imbalanced Node Classification** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2205.11332.pdf)\n 15. [TNNLS 2022] **Collaborative Decision-Reinforced Self-Supervision for Attributed Graph Clustering** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9777842)\n 16. [arXiv 2022] **Contrastive Graph Learning with Graph Convolutional Networks** [[paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-06555-2_7)\n 17. [TISPN 2022] **Fair Contrastive Learning on Graphs** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9779533)\n 18. [arXiv 2022] **SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks** [[paper]](https:\u002F\u002Fzepengzhang.com\u002FNotes\u002F2022\u002F20220507.pdf)\n 19. [arXiv 2022] **HCL: Hybrid Contrastive Learning for Graph-based Recommendation** [[paper]](https:\u002F\u002Fassets.amazon.science\u002F21\u002F8b\u002Fa804e89041f1a83bb1f77fa6aaee\u002Fhcl-hybrid-contrastive-learning-for-graph-based-recommendation.pdf)\n 20. [arXiv 2022] **Representation learning with function call graph transformations for malware open set recognition** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2205.07865.pdf)\n 21. [arXiv 2022] **Simple Contrastive Graph Clustering** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2205.07865.pdf)\n 22. [NCA 2022] **Self-supervised graph representation learning using multi-scale subgraph views contrast** [[paper]](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs00521-022-07299-x)\n 23. [ACL 2022] **JointCL: A Joint Contrastive Learning Framework for Zero-Shot Stance Detection** [[paper]](https:\u002F\u002Faclanthology.org\u002F2022.acl-long.7\u002F) \n 24. [IPM 2022] **Contrastive Graph Convolutional Networks with adaptive augmentation for text classification** [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS0306457322000681) \n 25. [PAKDD 2022] **Contrastive Attributed Network Anomaly Detection with Data Augmentation** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1007\u002F978-3-031-05936-0_35) \n 26. [DASFAA 2022] **CSGNN: Improving Graph Neural Networks with Contrastive Semi-supervised Learning** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1007\u002F978-3-031-00123-9_58)\n 27. [arXiv 2022] **Dynamic Graph Representation Based on Temporal and Contextual Contrasting** [[paper]](https:\u002F\u002Fassets.researchsquare.com\u002Ffiles\u002Frs-1588877\u002Fv1_covered.pdf?c=1651680782)\n 28. [DASFAA 2022] **Diffusion-Based Graph Contrastive Learning for Recommendation with Implicit Feedback** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1007\u002F978-3-031-00126-0_15)\n 29. [arXiv 2022] **FastGCL: Fast Self-Supervised Learning on Graphs via Contrastive Neighborhood Aggregation** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2205.00905.pdf)\n 30. [arXiv 2022] **RoSA: A Robust Self-Aligned Framework for Node-Node Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2204.13846.pdf)\n 31. [arXiv 2022] **Heterogeneous Graph Neural Networks using Self-supervised Reciprocally Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2205.00256.pdf)\n 32. [WSDM 2022] **JGCL: Joint Self-Supervised and Supervised Graph Contrastive Learning** [[paper]](https:\u002F\u002Fwww2022.thewebconf.org\u002FPaperFiles\u002F161.pdf)\n 33. [AAAI 2022] **SAIL: Self-Augmented Graph Contrastive Learning** [[paper]](https:\u002F\u002Fwww.aaai.org\u002FAAAI22Papers\u002FAAAI-8378.YuL.pdf)\n 34. [ICASSP 2022] **Graph Fine-Grained Contrastive Representation Learning** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9746085)\n 35. [arXiv 2022] **SCGC: Self-Supervised Contrastive Graph Clustering** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2204.12656.pdf)\n 36. [arXiv 2022] **A Content-First Benchmark for Self-Supervised Graph Representation Learning** [[paper]](https:\u002F\u002Fgraph-learning-benchmarks.github.io\u002Fassets\u002Fpapers\u002Fglb2022\u002FA_Content_First_Benchmark_for_Self_Supervised_Graph_Representation_Learning.pdf)\n 37. [SIGIR 2022] **Hypergraph Contrastive Collaborative Filtering** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2204.12200.pdf)\n 38. [WWW 2022] **Rumor Detection on Social Media with Graph Adversarial Contrastive Learning** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3485447.3511999)\n 39. [arXiv 2022] **A Review-aware Graph Contrastive Learning Framework for Recommendation** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2204.12063.pdf)\n 40. [WWW 2022] **Robust Self-Supervised Structural Graph Neural Network for Social Network Prediction** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3485447.3512182)\n 41. [arXiv 2022] **CGC: Contrastive Graph Clustering for Community Detection and Tracking** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2204.08504.pdf)\n 42. [TCyber 2022] **Multiview Deep Graph Infomax to Achieve Unsupervised Graph Embedding** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9758652)\n 43. [arXiv 2022] **MVGCNMDA: Multi-view Graph Augmentation Convolutional Network for Uncovering Disease-Related Microbes** [[paper]](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs12539-022-00514-2)\n 44. [arXiv 2022] **CERES: Pretraining of Graph-Conditioned Transformer for Semi-Structured Session Data** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2204.04303.pdf)\n 45. [arXiv 2022] **Self-Supervised Graph Neural Network for Multi-Source Domain Adaptation** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2204.05104.pdf)\n 46. [SIGIR 2022] **Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2112.08679)[[code]](https:\u002F\u002Fgithub.com\u002FCoder-Yu\u002FSELFRec)\n 47. [arXiv 2022] **Explanation Graph Generation via Pre-trained Language Models: An Empirical Study with Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2204.04813)\n 48. [arXiv 2022] **Augmentation-Free Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2204.04874)\n 49. [TCybern 2022] **Link-Information Augmented Twin Autoencoders for Network Denoising** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9745753)\n 50. [arXiv 2022] **Node Representation Learning in Graph via Node-to-Neighbourhood Mutual Information Maximization** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.12265)\n 51. [arXiv 2022] **GraphCoCo: Graph Complementary Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.12821)\n 52. [arXiv 2022] **Unsupervised Heterophilous Network Embedding via r-Ego Network Discrimination** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.10866.pdf)\n 53. [Bioinformatics 2022] **Supervised Graph Co-contrastive Learning for Drug-Target Interaction Prediction** [[paper]](https:\u002F\u002Facademic.oup.com\u002Fbioinformatics\u002Fadvance-article\u002Fdoi\u002F10.1093\u002Fbioinformatics\u002Fbtac164\u002F6551245?login=true)\n 54. [arXiv 2022] **Supervised Contrastive Learning with Structure Inference for Graph Classification** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.07691)\n 55. [arXiv 2022] **Defending Graph Convolutional Networks against Dynamic Graph Perturbations via Bayesian Self-supervision** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.03762.pdf)\n 57. [arXiv 2022] **Analyzing Heterogeneous Networks with Missing Attributes by Unsupervised Contrastive Learning** [[paper]](https:\u002F\u002Fyangliang.github.io\u002Fpdf\u002Ftnnls22.pdf)\n 58. [arXiv 2022] **Improving Molecular Contrastive Learning via Faulty Negative Mitigation and Decomposed Fragment Contrast** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.09346.pdf)\n 59. [arXiv 2022] **Contrastive Meta Learning with Behavior Multiplicity for Recommendation** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.08523.pdf)[[code]](https:\u002F\u002Fgithub.com\u002Fweiwei1206\u002FCML.git)\n 60. [arXiv 2022] **Fair Node Representation Learning via Adaptive Data Augmentation** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2201.08549.pdf)\n 61. [arXiv 2022] **Learning Graph Augmentations to Learn Graph Representations** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2201.09830.pdf)[[code]](https:\u002F\u002Fgithub.com\u002Fkavehhassani\u002Flg2ar)\n 62. [arXiv 2022] **Graph Data Augmentation for Graph Machine Learning: A Survey** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.08871.pdf)\n 63. [arXiv 2022] **Data Augmentation for Deep Graph Learning: A Survey** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2202.08235)\n 64. [arXiv 2022] **Adversarial Graph Contrastive Learning with Information Regularization** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.06491.pdf)\n 65. [arXiv 2022] **SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.03104.pdf)\n 66. [NeurIPS 2022] **Graph Self-supervised Learning with Accurate Discrepancy Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.02989.pdf)\n 67. [arXiv 2022] **Learning Robust Representation through Graph Adversarial Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2201.13025.pdf)\n 68. [arXiv 2022] **Self-supervised Graphs for Audio Representation Learning with Limited Labeled Data** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.00097.pdf)\n 69. [arXiv 2022] **Link Prediction with Contextualized Self-Supervision** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2201.10069.pdf)\n 70. [arXiv 2022] **Dual Space Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2201.07409.pdf)\n 71. [arXiv 2022] **Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagation** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2201.07986.pdf)\n 72. [arXiv 2022] **From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale Contrastive Learning Approach** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.05525)\n 73. [arXiv 2022] **Dual Space Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2201.07409)\n 74. [arXiv 2022] **Structure-Enhanced Heterogeneous Graph Contrastive Learning** [[paper]](https:\u002F\u002Fsxkdz.github.io\u002Ffiles\u002Fpublications\u002FSDM\u002FSTENCIL\u002FSTENCIL.pdf)\n 75. [bioRxiv 2022] **Towards Effective and Generalizable Fine-tuning for Pre-trained Molecular Graph Models** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002Fbiorxiv\u002Fearly\u002F2022\u002F02\u002F06\u002F2022.02.03.479055.full.pdf)\n 76. [SDM 2022] **Neural Graph Matching for Pre-training Graph Neural Networks** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.01597.pdf) [[code]](https:\u002F\u002Fgithub.com\u002FRUCAIBox\u002FGMPT)\n 77. [TNNLS 2022] **Analyzing Heterogeneous Networks with Missing Attributes by Unsupervised Contrastive Learning** [[paper]](https:\u002F\u002Fyangliang.github.io\u002Fpdf\u002Ftnnls22.pdf)\n 78. [WWW 2022] **Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.06200.pdf) [[code]](https:\u002F\u002Fgithub.com\u002FRUCAIBox\u002FNCL)\n 79. [WWW 2022] **ClusterSCL: Cluster-Aware Supervised Contrastive Learning on Graphs** [[paper]](https:\u002F\u002Fxiaojingzi.github.io\u002Fpublications\u002FWWW22-Wang-et-al-ClusterSCL.pdf)\n 80. [ICLR 2022] **Large-Scale Representation Learning on Graphs via Bootstrapping** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2102.06514.pdf)[[Code]](https:\u002F\u002Fgithub.com\u002FNamkyeong\u002FBGRL_Pytorch)\n 81. [ICLR 2022] **Automated Self-Supervised Learning for Graphs** [[paper]](https:\u002F\u002Fopenreview.net\u002Fforum?id=rFbR4Fv-D6-) [[code]](https:\u002F\u002Fgithub.com\u002FChandlerBang\u002FAutoSSL)\n 82. [AAAI 2022] **Self-supervised Graph Neural Networks via Diverse and Interactive Message Passing** [[paper]](https:\u002F\u002Fyangliang.github.io\u002Fpdf\u002Faaai22.pdf)\n 83. [AAAI 2022] **Augmentation-Free Self-Supervised Learning on Graphs** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2112.02472.pdf)[[code]](https:\u002F\u002Fgithub.com\u002FNamkyeong\u002FAFGRL)\n 84. [AAAI 2022] **Molecular Contrastive Learning with Chemical Element Knowledge Graph** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2112.00544.pdf)\n 85. [AAAI 2022] **Deep Graph Clustering via Dual Correlation Reduction** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2112.14772)[[code]](https:\u002F\u002Fgithub.com\u002Fyueliu1999\u002FDCRN)\n 86. [AAAI 2022] **Simple Unsupervised Graph Representation Learning** [[paper]](https:\u002F\u002Fwww.aaai.org\u002FAAAI22Papers\u002FAAAI-3999.MoY.pdf)\n 87. [WSDM 2022] **Bringing Your Own View: Graph Contrastive Learning without Prefabricated Data Augmentations** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2201.01702) [[code]](https:\u002F\u002Fgithub.com\u002FShen-Lab\u002FGraphCL_Automated)\n 88. [ICOIN 2022] **Adaptive Self-Supervised Graph Representation Learning** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9687176)\n 89. [NPL 2022] **How Does Bayesian Noisy Self-Supervision Defend Graph Convolutional Networks?** [[paper]](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11063-022-10750-8)\n 90. [SIGIR 2022] **Knowledge Graph Contrastive Learning for Recommendation** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.00976) [[code]](https:\u002F\u002Fgithub.com\u002Fyuh-yang\u002FKGCL-SIGIR22)\n \n ## Year 2021\n 1. [AAAI 2021] **Self-supervised hypergraph convolutional networks for session-based recommendation** [[paper]](https:\u002F\u002Fwww.aaai.org\u002FAAAI21Papers\u002FAAAI-1889.XiaX.pdf)\n 1. [arXiv 2021] **Pre-training Graph Neural Network for Cross Domain Recommendation** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2111.08268.pdf)\n 17. [arXiv 2021] **Augmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practices** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2111.03220.pdf)\n 18. [arXiv 2021] **Collaborative Graph Contrastive Learning: Data Augmentation Composition May Not be Necessary for Graph Representation Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2111.03262.pdf)\n 13. [arXiv 2021] **Multi-task Self-distillation for Graph-based Semi-Supervised Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2112.01174.pdf)\n 14. [arXiv 2021] **Subgraph Contrastive Link Representation Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2112.01165.pdf)\n 3. [arXiv 2021] **Multilayer Graph Contrastive Clustering Network** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2112.14021.pdf)\n 3. [arXiv 2021] **Graph Representation Learning via Contrasting Cluster Assignments** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2112.07934.pdf)\n 3. [arXiv 2021] **Graph-wise Common Latent Factor Extraction for Unsupervised Graph Representation Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2112.08830.pdf)\n 3. [arXiv 2021] **Bayesian Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2112.07823.pdf)\n 3. [arXiv 2021] **TCGL: Temporal Contrastive Graph for Self-supervised Video Representation Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2112.03587.pdf)\n 26. [arXiv 2021] **Graph Communal Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2110.14863.pdf)\n 27. [arXiv 2021] **Self-supervised Contrastive Attributed Graph Clustering** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2110.08264.pdf)\n 28. [arXiv 2021] **Self-Supervised Learning for Molecular Property Prediction** [[paper]](https:\u002F\u002Fchemrxiv.org\u002Fengage\u002Fapi-gateway\u002Fchemrxiv\u002Fassets\u002Forp\u002Fresource\u002Fitem\u002F61677becaa918db6bf2a31cb\u002Foriginal\u002Fself-supervised-learning-for-molecular-property-prediction.pdf)\n 29. [arXiv 2021] **RPT: Toward Transferable Model on Heterogeneous Researcher Data via Pre-Training** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2110.07336.pdf)\n 30. [arXiv 2021] **Scalable Consistency Training for Graph Neural Networks via Self-Ensemble Self-Distillation** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2110.06290.pdf)\n 31. [arXiv 2021] **PRE-TRAINING MOLECULAR GRAPH REPRESENTATION WITH 3D GEOMETRY** [[paper]](https:\u002F\u002Fwyliu.com\u002Fpapers\u002FGraphMVP.pdf) [[code]](https:\u002F\u002Fgithub.com\u002Fchao1224\u002FGraphMVP)\n 32. [arXiv 2021] **3D Infomax improves GNNs for Molecular Property Prediction** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.04126v1) [[code]](https:\u002F\u002Fgithub.com\u002FHannesStark\u002F3DInfomax)\n 34. [arXiv 2021] **Motif-based Graph Self-Supervised Learning for Molecular Property Prediction** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2110.00987.pdf)\n 35. [arXiv 2021] **Debiased Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2110.02027.pdf)\n 36. [arXiv 2021] **3D-Transformer: Molecular Representation with Transformer in 3D Space** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2110.01191.pdf)\n 37. [arXiv 2021] **Contrastive Pre-Training of GNNs on Heterogeneous Graphs** [[paper]](https:\u002F\u002Fyuanfulu.github.io\u002Fpublication\u002FCIKM-CPT.pdf)\n 38. [arXiv 2021] **Contrastive Graph Convolutional Networks for Hardware Trojan Detection in Third Party IP Cores** [[paper]](https:\u002F\u002Fpeople.cs.vt.edu\u002F~ramakris\u002Fpapers\u002FHardware_Trojan_Trigger_Detection__HOST2021.pdf)\n 39. [arXiv 2021] **GeomGCL: Geometric Graph Contrastive Learning for Molecular Property Prediction** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2109.11730.pdf)\n 40. [arXiv 2021] **Adaptive Multi-layer Contrastive Graph Neural Networks** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2109.14159.pdf)\n 42. [arXiv 2021] **Graph-MVP: Multi-View Prototypical Contrastive Learning for Multiplex Graphs** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2109.03560.pdf)\n 43. [arXiv 2021] **Hyper Meta-Path Contrastive Learning for Multi-Behavior Recommendation** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2109.02859.pdf)\n 44. [arXiv 2021] **Negative Sampling Strategies for Contrastive Self-Supervised Learning of Graph Representations** [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS0165168421003479)\n 45. [arXiv 2021] **Structure-Aware Hard Negative Mining for Heterogeneous Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2108.13886.pdf)\n 46. [arXiv 2021] **Spatio-Temporal Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2108.11873.pdf)\n 47. [arXiv 2021] **Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2108.09896.pdf)\n 92. [Arxiv 2021] **Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2101.06448) [[code]](https:\u002F\u002Fgithub.com\u002FCoder-Yu\u002FRecQ)\n 53. [arXiv 2021] **GCCAD: Graph Contrastive Coding for Anomaly Detection** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2108.07516.pdf)\n 54. [arXiv 2021] **Contrastive Self-supervised Sequential Recommendation with Robust Augmentation** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2108.06479.pdf)\n 55. [arXiv 2021] **RRLFSOR: An Efficient Self-Supervised Learning Strategy of Graph Convolutional Networks** [[paper]](https:\u002F\u002Farxiv.org\u002Fftp\u002Farxiv\u002Fpapers\u002F2108\u002F2108.07481.pdf)\n 59. [arXiv 2021] **Group Contrastive Self-Supervised Learning on Graphs** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2107.09787) \n 60. [arXiv 2021] **Multi-Level Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2107.02639)\n 62. [arXiv 2021] **From Canonical Correlation Analysis to Self-supervised Graph Neural Networks** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.12484) [[code]](https:\u002F\u002Fgithub.com\u002Fhengruizhang98\u002FCCA-SSG)\n 63. [arXiv 2021] **Evaluating Modules in Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.08171) [[code]](https:\u002F\u002Fgithub.com\u002Fthunlp\u002FOpenGCL)\n 70. [arXiv 2021] **Prototypical Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2106.09645.pdf)\n 71. [arXiv 2021] **Fairness-Aware Node Representation Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2106.05391.pdf)\n 72. [arXiv 2021] **Adversarial Graph Augmentation to Improve Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.05819)\n 73. [arXiv 2021] **Graph Barlow Twins: A self-supervised representation learning framework for graphs** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2106.02466.pdf)\n 74. [arXiv 2021] **Self-Supervised Graph Learning with Proximity-based Views and Channel Contrast** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2106.03723.pdf)\n 75. [arXiv 2021] **Self-supervised on Graphs: Contrastive, Generative,or Predictive** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2105.07342)\n 76. [arXiv 2021] **FedGL: Federated Graph Learning Framework with Global Self-Supervision** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2105.03170.pdf)\n 78. [arXiv 2021] **Hop-Count Based Self-Supervised Anomaly Detection on Attributed Networks** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2104.07917)\n 79. [arXiv 2021] **Representation Learning for Networks in Biology and Medicine: Advancements, Challenges, and Opportunities** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2104.04883)\n 80. [arXiv 2021] **Graph Representation Learning by Ensemble Aggregating Subgraphs via Mutual Information Maximization** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.13125)\n 81. [arXiv 2021] **Drug Target Prediction Using Graph Representation Learning via Substructures Contrast** [[paper]](https:\u002F\u002Fwww.preprints.org\u002Fmanuscript\u002F202103.0337\u002Fv1)\n 82. [arXiv 2021] **Self-supervised Auxiliary Learning for Graph Neural Networks via Meta-Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.00771)\n 83. [arXiv 2021] **Graph Self-Supervised Learning: A Survey** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.00111)\n 84. [arXiv 2021] **Towards Robust Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2102.13085.pdf)\n 85. [arXiv 2021] **Pre-Training on Dynamic Graph Neural Networks** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.12380)\n 86. [arXiv 2021] **Self-Supervised Learning of Graph Neural Networks: A Unified Review** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.10757)\n 61. [Openreview 2021] **An Empirical Study of Graph Contrastive Learning** [[paper]](https:\u002F\u002Fopenreview.net\u002Fforum?id=fYxEnpY-__G)\n 1. [BIBM 2021] **SGAT: a Self-supervised Graph Attention Network for Biomedical Relation Extraction** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9669699)\n 95. [BIBM 2021] **Molecular Graph Contrastive Learning with Parameterized Explainable Augmentations** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2021.12.03.471150v1)\n 5. [NeurIPS 2021 Workshop] **Self-Supervised GNN that Jointly Learns to Augment** [[paper]](https:\u002F\u002Fwww.researchgate.net\u002Fprofile\u002FZekarias-Kefato\u002Fpublication\u002F356997993_Self-Supervised_GNN_that_Jointly_Learns_to_Augment\u002Flinks\u002F61b75d88a6251b553ab64ff4\u002FSelf-Supervised-GNN-that-Jointly-Learns-to-Augment.pdf)\n 5. [NeurIPS 2021 Workshop] **Contrastive Embedding of Structured Space for Bayesian Optimisation** [[paper]](https:\u002F\u002Fopenreview.net\u002Fpdf?id=xFpkJUMS9te)\n 5. [NeurIPS 2021] **Enhancing Hyperbolic Graph Embeddings via Contrastive Learning** [[paper]](https:\u002F\u002Fsslneurips21.github.io\u002Ffiles\u002FCameraReady\u002FNeurIPS_2021_workshop_version2.pdf)\n 5. [NeurIPS 2021] **Graph Adversarial Self-Supervised Learning** [[paper]](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2021\u002Ffile\u002F7d3010c11d08cf990b7614d2c2ca9098-Paper.pdf)\n 6. [NeurIPS 2021] **Contrastive laplacian eigenmaps** [[paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Ffile\u002F2d1b2a5ff364606ff041650887723470-Paper.pdf)\n 7. [NeurIPS 2021] **Directed Graph Contrastive Learning** [[paper]](https:\u002F\u002Fzekuntong.com\u002Ffiles\u002Fdigcl_nips.pdf)[[code]](https:\u002F\u002Fgithub.com\u002Fflyingtango\u002FDiGCL)\n 8. [NeurIPS 2021] **Multi-view Contrastive Graph Clustering** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2110.11842.pdf)[[code]](https:\u002F\u002Fgithub.com\u002FPanern\u002FMCGC)\n 9. [NeurIPS 2021] **From Canonical Correlation Analysis to Self-supervised Graph Neural Networks** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2106.12484.pdf)[[code]](https:\u002F\u002Fgithub.com\u002Fhengruizhang98\u002FCCA-SSG)\n 10. [NeurIPS 2021] **InfoGCL: Information-Aware Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2110.15438.pdf)\n 11. [NeurIPS 2021] **Adversarial Graph Augmentation to Improve Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.05819)[[code]](https:\u002F\u002Fgithub.com\u002Fsusheels\u002Fadgcl)\n 12. [NeurIPS 2021] **Disentangled Contrastive Learning on Graphs** [[paper]](https:\u002F\u002Fopenreview.net\u002Fpdf?id=C_L0Xw_Qf8M)\n 20. [CIKM 2021] **Multimodal Graph Meta Contrastive Learning** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3459637.3482151)\n 21. [CIKM 2021] **Self-supervised Representation Learning on Dynamic Graphs** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3459637.3482389)\n 22. [CIKM 2021] **Rectifying Pseudo Labels: Iterative Feature Clustering for Graph Representation Learning** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3459637.3482469)\n 23. [CIKM 2021] **SGCL: Contrastive Representation Learning for Signed Graphs** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3459637.3482478)\n 24. [CIKM 2021] **Semi-Supervised and Self-Supervised Classification with Multi-View Graph Neural Networks** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3459637.3482477)\n 25. [CIKM 2021] **Social Recommendation with Self-Supervised Metagraph Informax Network** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3459637.3482480) [[code]](https:\u002F\u002Fgithub.com\u002FSocialRecsys\u002FSMIN)\n 48. [IJCAI 2021] **Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning** [[paper]](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2021\u002F0204.pdf)\n 49. [IJCAI 2021] **Pairwise Half-graph Discrimination: A Simple Graph-level Self-supervised Strategy for Pre-training Graph Neural Networks** [[paper]](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2021\u002F0371.pdf)\n 50. [IJCAI 2021] **CuCo: Graph Representation with Curriculum Contrastive Learning** [[paper]](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2021\u002F0317.pdf)\n 51. [IJCAI 2021] **Graph Debiased Contrastive Learning with Joint Representation Clustering** [[paper]](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2021\u002F0473.pdf)\n 52. [IJCAI 2021] **CSGNN: Contrastive Self-Supervised Graph Neural Network for Molecular Interaction Prediction** [[paper]](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2021\u002F0517.pdf)\n 56. [KDD 2021] **MoCL: Data-driven Molecular Fingerprint via Knowledge-aware Contrastive Learning from Molecular Graph** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3447548.3467186) [[code]](https:\u002F\u002Fgithub.com\u002Fillidanlab\u002FMoCL-DK)\n 57. [KDD 2021] **Contrastive Multi-View Multiplex Network Embedding with Applications to Robust Network Alignment** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3447548.3467227)\n 58. [KDD 2021] **Adaptive Transfer Learning on Graph Neural Networks** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2107.08765.pdf)\n 64. :fire:[ICML 2021] **Graph Contrastive Learning Automated** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.07594) [[code]](https:\u002F\u002Fgithub.com\u002FShen-Lab\u002FGraphCL_Automated)\n 66. [ICML 2021] **Self-supervised Graph-level Representation Learning with Local and Global Structure** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2106.04113) [[code]](https:\u002F\u002Fgithub.com\u002FDeepGraphLearning\u002FGraphLoG)\n 67. [KDD 2021] **Pre-training on Large-Scale Heterogeneous Graph** [[paper]](http:\u002F\u002Fwww.shichuan.org\u002Fdoc\u002F111.pdf)\n 68. [KDD 2021] **MoCL: Contrastive Learning on Molecular Graphs with Multi-level Domain Knowledge** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2106.04509.pdf)\n 69. [KDD 2021] **Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2105.09111) [[code]](https:\u002F\u002Fgithub.com\u002Fliun-online\u002FHeCo)\n 87. [WWW 2021 Workshop] **Iterative Graph Self-Distillation** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.12609)\n 88. [WWW 2021] **HDMI: High-order Deep Multiplex Infomax** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.07810) [[code]](https:\u002F\u002Fgithub.com\u002Fbaoyujing\u002FHDMI)\n 89. :fire:[WWW 2021] **Graph Contrastive Learning with Adaptive Augmentation** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.14945) [[code]](https:\u002F\u002Fgithub.com\u002FCRIPAC-DIG\u002FGCA)\n 90. [WWW 2021] **SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanism** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2101.08170) [[code]](https:\u002F\u002Fgithub.com\u002FRingBDStack\u002FSUGAR)\n 91. [WWW 2021] **Multi-view Graph Contrastive Representation Learning for Drug-Drug Interaction Prediction** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.11711) [[code]](https:\u002F\u002Fgithub.com\u002Fisjakewong\u002FMIRACLE)\n 93. :fire:[ICLR 2021] **How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision** [[paper]](https:\u002F\u002Fopenreview.net\u002Fforum?id=Wi5KUNlqWty) [[code]](https:\u002F\u002Fgithub.com\u002Fdongkwan-kim\u002FSuperGAT)\n 94. [WSDM 2021] **Pre-Training Graph Neural Networks for Cold-Start Users and Items Representation** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.07064) [[code]](https:\u002F\u002Fgithub.com\u002Fjerryhao66\u002FPretrain-Recsys)\n 41. [KBS 2021] **Multi-aspect self-supervised learning for heterogeneous information network** [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS095070512100736X)\n 33. [CVPR 2021] **Zero-Shot Learning via Contrastive Learning on Dual Knowledge Graphs** [[paper]](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2021W\u002FGSP-CV\u002Fpapers\u002FWang_Zero-Shot_Learning_via_Contrastive_Learning_on_Dual_Knowledge_Graphs_ICCVW_2021_paper.pdf)\n 2. [ICBD 2021] **Session-based Recommendation via Contrastive Learning on Heterogeneous Graph** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9671296)\n 4. [ICONIP 2021] **Concordant Contrastive Learning for Semi-supervised Node Classification on Graph** [[paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-030-92185-9_48)\n 15. [ICCSNT 2021] **Graph Data Augmentation based on Adaptive Graph Convolution for Skeleton-based Action Recognition** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9615451)\n 77. [IJCNN 2021] **Node Embedding using Mutual Information and Self-Supervision based Bi-level Aggregation** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2104.13014v1)\n \n ## Year 2020\n 1. [Openreview 2020] **Motif-Driven Contrastive Learning of Graph Representations** [[paper]](https:\u002F\u002Fopenreview.net\u002Fforum?id=qcKh_Msv1GP)\n 15. [Openreview 2020] **SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks** [[paper]](https:\u002F\u002Fopenreview.net\u002Fforum?id=a5KvtsZ14ev)\n 16. [Openreview 2020] **TopoTER: Unsupervised Learning of Topology Transformation Equivariant Representations** [[paper]](https:\u002F\u002Fopenreview.net\u002Fforum?id=9az9VKjOx00)\n 17. [Openreview 2020] **Graph-Based Neural Network Models with Multiple Self-Supervised Auxiliary Tasks** [[paper]](https:\u002F\u002Fopenreview.net\u002Fforum?id=hnJSgY7p33a)\n 19. [Openreview 2020] **Transfer Learning of Graph Neural Networks with Ego-graph Information Maximization** [[paper]](https:\u002F\u002Fopenreview.net\u002Fforum?id=J_pvI6ap5Mn)\n 1. [Arxiv 2020] **COAD: Contrastive Pre-training with Adversarial Fine-tuning for Zero-shot Expert Linking** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.11336) [[code]](https:\u002F\u002Fgithub.com\u002FBoChen-Daniel\u002FExpert-Linking)\n 12. [Arxiv 2020] **Distance-wise Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.07437)\n 23. :fire:[Arxiv 2020] **Self-supervised Learning on Graphs: Deep Insights and New Direction.** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.10141) [[code]](https:\u002F\u002Fgithub.com\u002FChandlerBang\u002FSelfTask-GNN)\n 24. :fire:[Arxiv 2020] **Deep Graph Contrastive Representation Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.04131)\n 29. [Arxiv 2020] **Self-supervised Training of Graph Convolutional Networks.** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.02380)\n 30. [Arxiv 2020] **Self-Supervised Graph Representation Learning via Global Context Prediction.** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2003.01604)\n 33. :fire:[Arxiv 2020] **Graph-Bert: Only Attention is Needed for Learning Graph Representations.** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2001.05140) [[code]](https:\u002F\u002Fgithub.com\u002Fanonymous-sourcecode\u002FGraph-Bert)\n 20. :fire:[NeurIPS 2020] **Self-Supervised Graph Transformer on Large-Scale Molecular Data** [[paper]](https:\u002F\u002Fdrug.ai.tencent.com\u002Fpublications\u002FGROVER.pdf)\n 21. [NeurIPS 2020] **Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.08294) [[code]](https:\u002F\u002Fgithub.com\u002Fmlvlab\u002FSELAR)\n 22. :fire:[NeurIPS 2020] **Graph Contrastive Learning with Augmentations** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.13902) [[code]](https:\u002F\u002Fgithub.com\u002FShen-Lab\u002FGraphCL)\n 25. :fire:[ICML 2020] **When Does Self-Supervision Help Graph Convolutional Networks?** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.09136) [[code]](https:\u002F\u002Fgithub.com\u002FShen-Lab\u002FSS-GCNs)\n 26. :fire:[ICML 2020] **Graph-based, Self-Supervised Program Repair from Diagnostic Feedback.** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.10636)\n 27. :fire:[ICML 2020] **Contrastive Multi-View Representation Learning on Graphs.** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.05582) [[code]](https:\u002F\u002Fgithub.com\u002Fkavehhassani\u002Fmvgrl)\n 28. [ICML 2020 Workshop] **Self-supervised edge features for improved Graph Neural Network training.** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.04777)\n 31. :fire:[KDD 2020] **GPT-GNN: Generative Pre-Training of Graph Neural Networks.** [[pdf]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.15437) [[code]](https:\u002F\u002Fgithub.com\u002Facbull\u002FGPT-GNN)\n 32. :fire:[KDD 2020] **GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training.** [[pdf]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.09963) [[code]](https:\u002F\u002Fgithub.com\u002FTHUDM\u002FGCC) \n 34. :fire:[ICLR 2020] **InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization.** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1908.01000) [[code]](https:\u002F\u002Fgithub.com\u002Ffanyun-sun\u002FInfoGraph)\n 35. :fire:[ICLR 2020] **Strategies for Pre-training Graph Neural Networks.** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1905.12265) [[code]](https:\u002F\u002Fgithub.com\u002Fsnap-stanford\u002Fpretrain-gnns)\n 36. :fire:[AAAI 2020] **Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labels.** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1902.11038)\n 1. [ICDM 2020] **Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.10273) [[code]](https:\u002F\u002Fgithub.com\u002Fyzjiao\u002FSubg-Con)\n \n ## Year 2019\n 1. [KDD 2019 Workshop] **SGR: Self-Supervised Spectral Graph Representation Learning.** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1811.06237)\n 1. [ICLR 2019 Workshop] **Can Graph Neural Networks Go \"Online\"? An Analysis of Pretraining and Inference.** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1905.06018)\n 1. [ICLR 2019 workshop] **Pre-Training Graph Neural Networks for Generic Structural Feature Extraction.** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1905.13728)\n 1. [Arxiv 2019] **Heterogeneous Deep Graph Infomax** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1911.08538) [[code]](https:\u002F\u002Fgithub.com\u002FYuxiangRen\u002FHeterogeneous-Deep-Graph-Infomax)\n 1. :fire:[ICLR 2019] **Deep Graph Informax.** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.10341) [[code]](https:\u002F\u002Fgithub.com\u002FPetarV-\u002FDGI)\n \n \n ## Other related papers\n  (implicitly using self-supersvied learning or applying graph neural networks in other domains)\n 1. [Arxiv 2020] **Self-supervised Learning: Generative or Contrastive.** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.08218)\n 1. [KDD 2020] **Octet: Online Catalog Taxonomy Enrichment with Self-Supervision.** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2006.10276.pdf)\n 1. [WWW 2020] **Structural Deep Clustering Network.** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3366423.3380214\n ) [[code]](https:\u002F\u002Fgithub.com\u002Fbdy9527\u002FSDCN)\n 1. [IJCAI 2019] **Pre-training of Graph Augmented Transformers for Medication Recommendation.** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1906.00346) [[code]](https:\u002F\u002Fgithub.com\u002Fjshang123\u002FG-Bert)\n 1. [AAAI 2020] **Unsupervised Attributed Multiplex Network Embedding** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1911.06750) [[code]](https:\u002F\u002Fgithub.com\u002Fpcy1302\u002FDMGI)\n 1. [WWW 2020] **Graph representation learning via graphical mutual information maximization** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3366423.3380112)\n 1. [NeurIPS 2017] **Inductive Representation Learning on Large Graphs** [[paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2017\u002Fhash\u002F5dd9db5e033da9c6fb5ba83c7a7ebea9-Abstract.html) [[code]](https:\u002F\u002Fgithub.com\u002Fwilliamleif\u002FGraphSAGE)\n 1. [NeurIPS 2016 Workshop] **Variational Graph Auto-Encoders** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1611.07308) [[code]](https:\u002F\u002Fgithub.com\u002Ftkipf\u002Fgae)\n 1. [WWW 2015] **LINE: Large-scale Information Network Embedding** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F2736277.2741093) [[code]](https:\u002F\u002Fgithub.com\u002Ftangjianpku\u002FLINE)\n 1. [KDD 2014] **DeepWalk: Online Learning of Social Representations** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F2623330.2623732) [[code]](https:\u002F\u002Fgithub.com\u002Fphanein\u002Fdeepwalk)\n \n ## Acknowledgement\n \n This page is contributed and maintained by [Wei Jin](http:\u002F\u002Fcse.msu.edu\u002F~jinwei2\u002F)(joe.weijin@gmail.com), [Yuning You](https:\u002F\u002Fyyou1996.github.io\u002F)(yuning.you@tamu.edu) and [Yingheng Wang](https:\u002F\u002Fisjakewong.github.io\u002F)(jakewyh@163.com).\n","# 令人惊叹的自监督图神经网络\n\n![欢迎提交PR](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FPRs-Welcome-green)  [![Awesome](https:\u002F\u002Fawesome.re\u002Fbadge.svg)](https:\u002F\u002Fawesome.re) ![星标](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fstars\u002FChandlerBang\u002Fawesome-self-supervised-gnn?color=yellow)  ![叉子](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fforks\u002FChandlerBang\u002Fawesome-self-supervised-gnn?color=blue&label=Fork)\n\n本仓库收录了关于**图神经网络（GNN）上的自监督学习**的相关论文，并按发表年份进行了分类。\n\n我们会尽量保持这份列表的更新。如果您发现任何错误或遗漏的论文，请随时提出Issue或发送Pull Request。\n\n注：:fire: 表示该论文被广泛引用（例如，超过80次引用）。代码可在`get_hot.py`中找到。\n\n## 2024年\n1. [ICASSP 2024] **用于社区检测的对比式深度非负矩阵分解** [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.02357) [[代码]](https:\u002F\u002Fgithub.com\u002F6lyc\u002FCDNMF)\n\n## Year 2023\n1. [ICLR 2023] **Empowering Graph Representation Learning with Test-Time Graph Transformation** [[paper]](https:\u002F\u002Fopenreview.net\u002Fforum?id=Lnxl5pr018) [[code]](https:\u002F\u002Fgithub.com\u002FChandlerBang\u002FGTrans)\n1. [ICLR 2023] **Multi-task Self-supervised Graph Neural Networks Enable Stronger Task Generalization** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2210.02016.pdf) [[code]](https:\u002F\u002Fgithub.com\u002Fjumxglhf\u002FParetoGNN)\n1. [AAAI 2023] **Eliciting Structural and Semantic Global Knowledge in Unsupervised Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.08480) [[code]](https:\u002F\u002Fgithub.com\u002Fkaize0409\u002FS-3-CL)\n1. [arXiv 2023] **Truncated Affinity Maximization: One-class Homophily Modeling for Graph Anomaly Detection** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2306.00006.pdf)\n1. [ICASSP 2023] **Contrastive Learning at the Relation and Event Level for Rumor Detection** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F10096567)\n1. [arXiv 2023] **AmGCL: Feature Imputation of Attribute Missing Graph via Self-supervised Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2305.03741.pdf)\n1. [arXiv 2023] **SEGA: Structural Entropy Guided Anchor View for Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2305.04501.pdf)\n1. [arXiv 2023] **CSGCL: Community-Strength-Enhanced Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2305.04658.pdf)\n1. [TKDE 2023] **MINING: Multi-Granularity Network Alignment Based on Contrastive Learning** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F10120956)\n1. [ICASSP 2023] **Select The Best: Enhancing Graph Representation with Adaptive Negative Sample Selection** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F10095586)\n1. [ICASSP 2023] **Graph Contrastive Learning with Learnable Graph Augmentation** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F10095511)\n1. [arXiv 2023] **FormNetV2: Multimodal Graph Contrastive Learning for Form Document Information Extraction** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2305.02549.pdf)\n1. [INS 2023] **A fairness-aware graph contrastive learning recommender framework for social tagging systems** [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0020025523006497)\n1. [arXiv 2023] **Improving Knowledge Graph Entity Alignment with Graph Augmentation** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2304.14585.pdf)\n1. [WWW 2023] **Graph Self-supervised Learning with Augmentation-aware Contrastive Learning** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3543507.3583246)\n1. [arXiv 2023] **A Systematic Survey of Chemical Pre-trained Models** [[paper]](https:\u002F\u002Fsxkdz.github.io\u002Ffiles\u002Fpublications\u002FIJCAI\u002FCPM\u002FCPM.pdf)\n1. [WWW 2023] **Self-Supervised Teaching and Learning of Representations on Graphs** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3543507.3583441)\n1. [TKDE 2023] **Progressive Hard Negative Masking: From Global Uniformity to Local Tolerance** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F10111083)\n1. [KBS 2023] **ST-A-PGCL: Spatiotemporal adaptive periodical graph contrastive learning for traffic prediction under real scenarios** [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0950705123003416)\n1. [WWW 2023] **SeeGera: Self-supervised Semi-implicit Graph Variational Auto-encoders with Masking** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3543507.3583245)\n1. [INS 2023] **Self-supervised Contrastive Learning on Heterogeneous Graphs with Mutual Constraints of Structure and Feature** [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0020025523006114)\n1. [Scientific Reports 2023] **A multi-view contrastive learning for heterogeneous network embedding** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-023-33324-7)\n1. [WWW 2023] **Automated Spatio-Temporal Graph Contrastive Learning** [[paper]](https:\u002F\u002Fzhengwang125.github.io\u002Fpaper\u002FSTGCL_WWW23.pdf)\n1. [arXiv 2023] **Capturing Fine-grained Semantics in Contrastive Graph Representation Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2304.11658.pdf)\n1. [arXiv 2023] **Decouple Graph Neural Networks: Train Multiple Simple GNNs Simultaneously Instead of One** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2304.10126.pdf)\n1. [arXiv 2023] **ID-MixGCL: Identity Mixup for Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2304.10045.pdf)\n1. [Bioinformatics 2023] **Molecular Property Prediction by Contrastive Learning with Attention-Guided Positive Sample Selection** [[paper]](https:\u002F\u002Fwatermark.silverchair.com\u002Fbtad258.pdf?token=AQECAHi208BE49Ooan9kkhW_Ercy7Dm3ZL_9Cf3qfKAc485ysgAAAwcwggMDBgkqhkiG9w0BBwagggL0MIIC8AIBADCCAukGCSqGSIb3DQEHATAeBglghkgBZQMEAS4wEQQMdupp3nabpyWrY1TvAgEQgIICuslU3gktfD9EQ9YOuajKd5nL5RNR0eI5eAOngtpUfOUcqcGOQONeb7Lznmgz8twSmMS13_U5bKR6FRKpce_1s9teGI5K7J6JLdx_sHrlBZGP8m1xMzk7soYc8pGHVsgbKwusPR5rkaRd-JykOSM3eIn_5IQgqqJ2RYmtcymvywcuGV1tA__M44XepfMuzHcC9q5h8NuWaWXmMzode9nlFyO0eacGBbSG8zvaH97K65aD734tbaUW60Do6fS_5yq9kRMFV3EPqnJwJ0iJ72o3ZFSNBjxb2yDH1kd_TZbkmio6LC6ZH8mrubOKxGDhrzjruSEpe1Fs54BzZfrqrGbmv8LB9sWxbSXAitKbMGnFb1WxyBF6cyB9g1AyqGYJEMr7HM7yBC9UOmff_s1kH-Avd_L8ZfzyhVqDvUyIgJc39Nlw6Eju3stlDuKMIwwWBI6qWHkc_nEd_0u7n1ssxbBydo63PZKmNbtsq36l7wN0goc_sWYXy9AyMu0ROFNLfWSe6n6k_u7DIyRlm7GPzOrx3CEaCWq_8uw1Pkvygflhz4aktGzWUBxodPezX4ToO2_9Q7IP9IjccsCI_zcr38C3EaHhtZf4yXFCowrL7C7MOLq9yo_9huTv3UJ_qq0dL7UCnJgrkI0kK7pkljnSu2gd0iuxwftCnphrXiw79xJwVUXTvbWKe_xxoh_XHllwhztCmPFYFbmwB-1A2gYpWq2fnNl7LxxvnioJCuoz9mwaFXN6tLwCCPkZa-GdakTaoHoU30JGMvrgdyhhFU30mUN5NOyWaoOLcqFLy8y-mO_V07uUGmMkS3SHM0j-qYEdjVEddM7QxbW5JW28EkL3L97BWaBohCHcj0jiS7pzteOwzZ4e3WWhghFX1pDGeFvvhzv5xCobn5TPFV1N9qk7I7QrEZSjAg1epeLNvohj)\n1. [AISTAT 2023] **Learning Robust Graph Neural Networks with Limited Supervision** [[paper]](https:\u002F\u002Fproceedings.mlr.press\u002Fv206\u002Falchihabi23a\u002Falchihabi23a.pdf)\n1. [TNNLS 2023] **Demystifying and Mitigating Bias for Node Representation Learning** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F10103678)\n1. [BICTA 2023] **Graph Contrastive Learning with Intrinsic Augmentations** [[paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-981-99-1549-1_27)\n1. [arXiv 2023] **GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learner** [[paper]](https:\u002F\u002Fepubs.siam.org\u002Fdoi\u002Fpdf\u002F10.1137\u002F1.9781611977653.ch19)\n1. [arXiv 2023] **Adversarial Hard Negative Generation for Complementary Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2304.04779.pdf)\n1. [INS 2023] **INS-GNN: Improving Graph Imbalance Learning with Self-Supervision** [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0020025523005042)\n1. [TNNLS 2023] **Dual Contrastive Learning Network for Graph Clustering** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F10097557)\n1. [arXiv 2023] **RARE: Robust Masked Graph Autoencoder** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2304.01507.pdf)\n1. [TKDE 2023] **Maximizing Mutual Information Across Feature and Topology Views for Representing Graphs** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F10093032)\n1. [arXiv 2023] **When to Pre-Train Graph Neural Networks? An Answer from Data Generation Perspective!** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.16458)\n1. [KBS 2023] **Class-homophilic-based data augmentation for improving graph neural networks** [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS095070512300268X)\n1. [arXiv 2023] **Structural Imbalance Aware Graph Augmentation Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2303.13757.pdf)\n1. [arXiv 2023] **Hybrid Augmented Automated Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2303.15182.pdf)\n1. [arXiv 2023] **Decoupling Graph Neural Network with Contrastive Learning for Fraud Detection** [[paper]](https:\u002F\u002Flinmengsysu.github.io\u002Fslides\u002Fmain.pdf)\n1. [arXiv 2023] **Data-Centric Learning from Unlabeled Graphs with Diffusion Model** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2303.10108.pdf)\n1. [TPAMI 2023] **Unsupervised Learning of Graph Matching With Mixture of Modes Via Discrepancy Minimization** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F10073537)\n1. [arXiv 2023] **NESS: Learning Node Embeddings from Static SubGraphs** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2303.08958.pdf)\n1. [Sensors 2023] **A Robust Automated Analog Circuits Classification Involving a Graph Neural Network and a Novel Data Augmentation Strategy** [[paper]](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F23\u002F6\u002F2989)\n1. [arXiv 2023] **Contrastive knowledge integrated graph neural networks for Chinese medical text classification** [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0952197623002415)\n1. [arXiv 2023] **CHGNN: A Semi-Supervised Contrastive Hypergraph Learning Network** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2303.06213.pdf)\n1. [arXiv 2023] **Contrastive Learning under Heterophily** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2303.06344.pdf)\n1. [arXiv 2023] **Structure-Aware Group Discrimination with Adaptive-View Graph Encoder: A Fast Graph Contrastive Learning Framework** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2303.05231.pdf)\n1. [TNNLS 2023] **Self-supervised Learning IoT Device Features with Graph Contrastive Neural Network for Device Classification in Social Internet of Things** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F10059194)\n1. [TKDE 2023] **Feature-Level Deeper Self-Attention Network With Contrastive Learning for Sequential Recommendation** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F10059216)\n1. [AAAI 2023] **Recommend What to Cache: a Simple Self-supervised Graph-based Recommendation Framework for Edge Caching Network** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2302.14438.pdf)\n1. [arXiv 2023] **Self-Supervised Interest Transfer Network via Prototypical Contrastive Learning for Recommendation** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2302.14438.pdf)\n1. [arXiv 2023] **SGL-PT: A Strong Graph Learner with Graph Prompt Tuning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2302.12449.pdf)\n1. [CIS 2023] **SimGRL: a simple self-supervised graph representation learning framework via triplets** [[paper]](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs40747-023-00997-6)\n1. [WSDM 2023] **Self-Supervised Group Graph Collaborative Filtering for Group Recommendation** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3539597.3570400)\n1. [WSDM 2023] **S2GAE: Self-Supervised Graph Autoencoders are Generalizable Learners with Graph Masking** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3539597.3570404)\n1. [WSDM 2023] **Heterogeneous Graph Contrastive Learning for Recommendation** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3539597.3570484)\n1. [Nature Communications Chemistry] **Hierarchical Molecular Graph Self-Supervised Learning for property prediction** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs42004-023-00825-5)\n1. [arXiv 2023] **Wiener Graph Deconvolutional Network Improves Graph Self-Supervised Learning** [[paper]](https:\u002F\u002Fwww.researchgate.net\u002Fprofile\u002FJia-Li-127\u002Fpublication\u002F368543822_Wiener_Graph_Deconvolutional_Network_Improves_Graph_Self-Supervised_Learning\u002Flinks\u002F63edebc419130a1a4a830593\u002FWiener-Graph-Deconvolutional-Network-Improves-Graph-Self-Supervised-Learning.pdf)\n1. [arXiv 2023] **Heterogeneous Social Event Detection via Hyperbolic Graph Representations** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2302.10362.pdf)\n1. [arXiv 2023] **LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2302.08191.pdf)\n1. [arXiv 2023] **GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2302.08043.pdf)\n1. [Pattern Recognition] **Dual-Channel Graph Contrastive Learning for Self-Supervised Graph-Level Representation Learning** [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0031320323001486)\n1. [NCA 2023] **Self-supervised contrastive learning for heterogeneous graph based on multi-pretext tasks** [[paper]](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs00521-023-08234-4)\n1. [arXiv 2023] **STERLING: Synergistic Representation Learning on Bipartite Graphs** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2302.05428.pdf)\n 1. [ICLR 2023] **Multi-task Self-supervised Graph Neural Networks Enable Stronger Task Generalization** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2210.02016.pdf)\n1. [WBD 2023] **Mixed-Order Heterogeneous Graph Pre-training for Cold-Start Recommendation** [[paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-25201-3_14)\n1. [arXiv 2023] **Explainable Action Prediction through Self-Supervision on Scene Graphs** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2302.03477.pdf)\n1. [arXiv 2023] **Spectral Augmentations for Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2302.02909.pdf)\n1. [RS 2023] **Representing Spatial Data with Graph Contrastive Learning** [[paper]](https:\u002F\u002Furldefense.com\u002Fv3\u002F__https:\u002F\u002Fscholar.google.com\u002Fscholar_url?url=https:**Awww.mdpi.com*2072-4292*15*4*880*pdf&hl=en&sa=X&d=18081949848644790374&ei=UtHkY-wUjdbJBK-AnIgN&scisig=AAGBfm2HRbUL2s5kW_fO96HIgBt-0lesJg&oi=scholaralrt&hist=Pv-V2igAAAAJ:16610178827432183357:AAGBfm3PSUTRAat5lSIOYWJJQSKiKvjk4g&html=&pos=1&folt=cit__;Ly8vLy8vLw!!KwNVnqRv!DcYtDY-xLzHkhx2yQ32kw_CetJ1VrPiy0H9Hilie6oEU0a9OMbDAWoV9kq6mhcDPope5FTQwyDvFJ1YT8B6R9su2t7P1Rg$)\n1. [ACLF 2023] **KE-GCL: Knowledge Enhanced Graph Contrastive Learning for Commonsense Question Answering** [[paper]](https:\u002F\u002Faclanthology.org\u002F2022.findings-emnlp.6.pdf)\n1. [TNNLS 2023] **GRLC: Graph Representation Learning With Constraints** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F10036344)\n1. [ESA 2023] **Contrastive graph clustering with adaptive filter** [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS095741742300146X)\n1. [arXiv 2023] **Biomedical Interaction Prediction with Adaptive Line Graph Contrastive Learning** [[paper]](https:\u002F\u002Fwww.mdpi.com\u002F2227-7390\u002F11\u002F3\u002F732)\n1. [arXiv 2023] **Affinity Uncertainty-based Hard Negative Mining in Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2301.13340.pdf)\n1. [arXiv 2023] **Self-supervised Semi-implicit Graph Variational Auto-encoders with Masking** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2301.12458.pdf)\n1. [ACM Trans. Web 2023] **Contrastive Graph Similarity Networks** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fpdf\u002F10.1145\u002F3580511)\n1. [ICBD 2023] **Predictive Masking for Semi-Supervised Graph Contrastive Learning** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F10020970)\n1. [TNNLS 2023] **Graph Representation Learning With Adaptive Metric** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F10025823)\n1. [RAL 2023] **Self-Supervised Local Topology Representation for Random Cluster Matching** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F10021967)\n1. [KBS 2023] **CrysGNN: Distilling pre-trained knowledge to enhance property prediction for crystalline materials** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2301.05852.pdf)\n1. [Entropy 2023] **A Semantic-Enhancement-Based Social Network User-Alignment Algorithm** [[paper]](https:\u002F\u002Fwww.mdpi.com\u002F1099-4300\u002F25\u002F1\u002F172)\n1. [KBS 2023] **Cross-view temporal graph contrastive learning for session-based recommendation** [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0950705123000540)\n1. [PR 2023] **Robust Image Clustering via Context-aware Contrastive Graph Learning** [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0031320323000419)\n1. [ICMLCS 2023] **AP-GCL: Adversarial Perturbation on Graph Contrastive Learning** [[paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-20096-0_47)\n1. [arXiv 2023] **Signed Directed Graph Contrastive Learning with Laplacian Augmentation** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2301.05163.pdf)\n1. [OJCS 2023] **SC-FGCL: Self-adaptive Cluster-based Federal Graph Contrastive Learning** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?arnumber=10015148)\n1. [BIB 2023] **CasANGCL: pre-training and fine-tuning model based on cascaded attention network and graph contrastive learning for molecular property prediction** [[paper]](https:\u002F\u002Facademic.oup.com\u002Fbib\u002Fadvance-article\u002Fdoi\u002F10.1093\u002Fbib\u002Fbbac566\u002F6966532)\n1. [AAAI 2023] **Spectral Feature Augmentation for Graph Contrastive Learning and Beyond** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2212.01026)\n1. [Entropy 2023] **Self-Supervised Node Classification with Strategy and Actively Selected Labeled Set** [[paper]](https:\u002F\u002Furldefense.com\u002Fv3\u002F__https:\u002F\u002Fscholar.google.com\u002Fscholar_url?url=https:**Awww.mdpi.com*1099-4300*25*1*30*pdf&hl=en&sa=X&d=13649462741514245070&ei=66yqY9q-NY_mmgHdka7oCw&scisig=AAGBfm0m2E6wg_90swKhBWYDrZsXMBr2kA&oi=scholaralrt&hist=Pv-V2igAAAAJ:16610178827432183357:AAGBfm3PSUTRAat5lSIOYWJJQSKiKvjk4g&html=&pos=0&folt=cit__;Ly8vLy8vLw!!KwNVnqRv!FbRTWxTuNHDzvvuiJFFzysRQQ3C08EMs3qJTdLHxTA4E2WK7FjMv32fbi6T1irhYspBlmsafx0xexY4FKuao4dHXv3q7hw$)\n \n ## Year 2022\n 1. [NeurIPS 2022] **Generalized Laplacian Eigenmaps** [[paper]](https:\u002F\u002Fopenreview.net\u002Fforum?id=HjicdpP-Nth)\n 1. [KDD 2022] **COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive Learning** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3534678.3539425)\n 1. [ITBE 2022] **Contrastive Multi-view Composite Graph Convolutional Networks Based on Contribution Learning for Autism Spectrum Disorder Classification** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9999336)\n 1. [IEEE Access 2022] **ROME: A Graph Contrastive Multi-view Framework from Hyperbolic Angular Space for MOOCs Recommendation** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?arnumber=10001755)\n 1. [arXiv 2022] **Heterogeneous Graph Contrastive Learning with Meta-path Contexts and Weighted Negative Samples** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2212.13847.pdf)\n 1. [arXiv 2022] **MolCPT: Molecule Continuous Prompt Tuning to Generalize Molecular Representation Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2212.10614.pdf)\n 1. [arXiv 2022] **Toward Improved Generalization: Meta Transfer of Self-supervised Knowledge on Graphs** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2212.08217.pdf)\n 1. [arXiv 2022] **Coarse-to-Fine Contrastive Learning on Graphs** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2212.06423.pdf)\n 1. [arXiv 2022] **MA-GCL: Model Augmentation Tricks for Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2212.07035.pdf)\n 1. [arXiv 2022] **Mul-GAD: a semi-supervised graph anomaly detection framework via aggregating multi-view information** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2212.05478.pdf)\n 1. [arXiv 2022] **Localized Contrastive Learning on Graphs** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2212.04604.pdf)\n 1. [arXiv 2022] **Alleviating neighbor bias: augmenting graph self-supervise learning with structural equivalent positive samples** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2212.04365.pdf)\n 1. [arXiv 2022] **Self-supervised Graph Representation Learning for Black Market Account Detection** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2212.02679.pdf)\n 1. [arXiv 2022] **Contrastive Deep Graph Clustering with Learnable Augmentation** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2212.03559.pdf)\n 1. [arXiv 2022] **Graph Anomaly Detection via Multi-Scale Contrastive Learning Networks with Augmented View** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2212.00535.pdf)\n 1. [arXiv 2022] **Self Supervised Clustering of Traffic Scenes using Graph Representations** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2211.15508.pdf)\n 1. [arXiv 2022] **Graph Contrastive Learning for Materials** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2211.13408.pdf)\n 1. [arXiv 2022] **Link Prediction with Non-Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2211.14394.pdf)\n 1. [IJMIR 2022] **TCKGE: Transformers with contrastive learning for knowledge graph embedding** [[paper]](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs13735-022-00256-3)\n 1. [arXiv 2022] **Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily Discriminating** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2211.14065.pdf)\n 1. [Neural Networks 2022] **Unsupervised graph-level representation learning with hierarchical contrasts** [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0893608022004609)\n 1. [arXiv 2022] **Relation-dependent Contrastive Learning with Cluster Sampling for Inductive Relation Prediction** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2211.12266.pdf)\n 1. [arXiv 2022] **Relational Symmetry based Knowledge Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2211.10738.pdf)\n 1. [arXiv 2022] **Towards Generalizable Graph Contrastive Learning: An Information Theory Perspective** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2211.10929.pdf)\n 1. [arXiv 2022] **Can Single-Pass Contrastive Learning Work for Both Homophilic and Heterophilic Graph?** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2211.10890.pdf)\n 1. [SIGSPATIAL 2022] **When Do Contrastive Learning Signals Help Spatio-Temporal Graph Forecasting?** [[paper]](http:\u002F\u002Furban-computing.com\u002Fpdf\u002FSTGCL_SIGSPATIAL_22.pdf)\n 1. [Scientific Reports 2022] **Deep graph level anomaly detection with contrastive learning** [[paper]](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-022-22086-3)\n 1. [TII 2022] **Semi-supervised machine fault diagnosis fusing unsupervised graph contrastive learning** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9944187)\n 1. [KBS 2022] **SMGCL: Semi-supervised Multi-view Graph Contrastive Learning** [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0950705122012163)\n 1. [arXiv 2022] **Unsupervised Graph Contrastive Learning with Data Augmentation for Malware Classification** [[paper]](https:\u002F\u002Fwww.researchgate.net\u002Fprofile\u002FYun-Gao-48\u002Fpublication\u002F365275847_Unsupervised_Graph_Contrastive_Learning_with_Data_Augmentation_for_Malware_Classification\u002Flinks\u002F636cec632f4bca7fd04b9a26\u002FUnsupervised-Graph-Contrastive-Learning-with-Data-Augmentation-for-Malware-Classification.pdf)\n 1. [IJCRS 2022] **Multi-scale Subgraph Contrastive Learning for Link Prediction** [[paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-21244-4_16)\n 1. [arXiv 2022] **Flaky Performances when Pretraining on Relational Databases** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2211.05213.pdf)\n 1. [arXiv 2022] **GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2211.04208.pdf)\n 1. [ATKDD 2022] **Ada-MIP: Adaptive Self-supervised Graph Representation Learning via Mutual Information and Proximity Optimization** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fpdf\u002F10.1145\u002F3568165)\n 1. [arXiv 2022] **Graph Contrastive Learning with Implicit Augmentations** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2211.03710.pdf)\n 1. [Information Sciences 2022] **Contrastive Graph Neural Network-based Camouflaged Fraud Detector** [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0020025522011926)\n 1. [arXiv 2022] **DyG2Vec: Representation Learning for Dynamic Graphs with Self-Supervision** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2210.16906.pdf)\n 1. [arXiv 2022] **Federated Graph Representation Learning using Self-Supervision** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2210.15120.pdf)\n 1. [arXiv 2022] **Benchmark of Self-supervised Graph Neural Networks** [[paper]](https:\u002F\u002Faaltodoc.aalto.fi\u002Fbitstream\u002Fhandle\u002F123456789\u002F116441\u002Fmaster_Wang_Haishan_2022.pdf?sequence=2)\n 1. [arXiv 2022] **Line Graph Contrastive Learning for Link Prediction** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2210.13795.pdf)\n 1. [TDSC 2022] **FewM-HGCL: Few-Shot Malware Variants Detection Via Heterogeneous Graph Contrastive Learning** [[paper]](https:\u002F\u002Fwww.computer.org\u002Fcsdl\u002Fjournal\u002Ftq\u002F5555\u002F01\u002F09928211\u002F1HJuUzzFey4)\n 1. [arXiv 2022] **Self-supervised Graph-based Point-of-interest Recommendation** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2210.12506.pdf)\n 1. [IJMLC 2022] **Hybrid sampling-based contrastive learning for imbalanced node classification** [[paper]](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs13042-022-01677-6)\n 1. [CIKM 2022] **Temporality-and Frequency-aware Graph Contrastive Learning for Temporal Network** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3511808.3557469)\n 1. [CIKM 2022] **Towards Self-supervised Learning on Graphs with Heterophily** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3511808.3557478)\n 1. [ISWC 2022] **HCL: Improving Graph Representation with Hierarchical Contrastive Learning** [[paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-19433-7_7)\n 1. [CIKM 2022] **Cognize Yourself: Graph Pre-Training via Core Graph Cognizing and Differentiating** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3511808.3557259)\n 1. [CIKM 2022] **AdaGCL: Adaptive Subgraph Contrastive Learning to Generalize Large-scale Graph Training** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3511808.3557228)\n 1. [CIKM 2022] **Look Twice as Much as You Say: Scene Graph Contrastive Learning for Self-Supervised Image Caption Generation** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3511808.3557382)\n 1. [CIKM 2022] **Malicious Repositories Detection with Adversarial Heterogeneous Graph Contrastive Learning** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3511808.3557384)\n 1. [ICEBE 2022] **Self-supervised Heterogeneous Graph Pre-training Based on Structural Clustering** [[paper]](https:\u002F\u002Fconferences.computer.org\u002Ficebe\u002F2022\u002Ficebe2022-proceedings\u002FKnowledge%20Graph%20Completion%20based%20on%20Hyperbolic%20Graph%20Contrastive%20Attention%20Network.pdf)\n 1. [arXiv 2022] **Self-supervised Heterogeneous Graph Pre-training Based on Structural Clustering** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2210.10462.pdf)\n 1. [NeurIPS 2022] **Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.03801) [[code]](https:\u002F\u002Fgithub.com\u002Fweitianxin\u002FHyperGCL)\n 1. [ICCL 2022] **Modeling Intra-and Inter-Modal Relations: Hierarchical Graph Contrastive Learning for Multimodal Sentiment Analysis** [[paper]](https:\u002F\u002Faclanthology.org\u002F2022.coling-1.622.pdf)\n 1. [TKDE 2022] **Adversarial Contrastive Learning for Evidence-aware Fake News Detection with Graph Neural Networks** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2210.05498.pdf)\n 1. [MM 2022] **Simple Self-supervised Multiplex Graph Representation Learning** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3503161.3547949)\n 1. [TMM 2022] **Self-consistent Contrastive Attributed Graph Clustering with Pseudo-label Prompt** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9914670)\n 1. [NeurIPS 2022] **Uncovering the Structural Fairness in Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2210.03011.pdf)\n 1. [NeurIPS 2022] **Revisiting Graph Contrastive Learning from the Perspective of Graph Spectrum** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2210.02330.pdf)\n 1. [arXiv 2022] **Heterogeneous Graph Contrastive Multi-view Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2210.00248.pdf)\n 1. [arXiv 2022] **Automated Graph Self-supervised Learning via Multi-teacher Knowledge Distillation** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2210.02099.pdf)\n 1. [arXiv 2022] **Prompt Tuning for Graph Neural Networks** [[paper]](https:\u002F\u002Fweb10.arxiv.org\u002Fpdf\u002F2209.15240.pdf)\n 1. [arXiv 2022] **Improving Molecular Pretraining with Complementary Featurizations** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2209.15101.pdf)\n 1. [arXiv 2022] **Graph Soft-Contrastive Learning via Neighborhood Ranking** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2209.13964.pdf)\n 1. [EDBT 2022] **Spatial Structure-Aware Road Network Embedding via Graph Contrastive Learning** [[paper]](https:\u002F\u002Fopenproceedings.org\u002F2023\u002Fconf\u002Fedbt\u002Fpaper-193.pdf)\n 1. [arXiv 2022] **Adversarial Cross-View Disentangled Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2209.07699.pdf)\n 1. [Neurocomputing 2022] **Motifs-based Recommender System via Hypergraph Convolution and Contrastive Learning** [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0925231222011948)\n 1. [TNNLS 2022] **Graph Representation Learning for Large-Scale Neuronal Morphological Analysis** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9895206)\n 1. [ECML-PKDD 2022] **Self-supervised Graph Learning with Segmented Graph Channels** [[paper]](https:\u002F\u002F2022.ecmlpkdd.org\u002Fwp-content\u002Fuploads\u002F2022\u002F09\u002Fsub_216.pdf)\n 1. [ECML-PKDD 2022] **Graph Contrastive Learning with Adaptive Augmentation for Recommendation** [[paper]](https:\u002F\u002F2022.ecmlpkdd.org\u002Fwp-content\u002Fuploads\u002F2022\u002F09\u002Fsub_650.pdf)\n 1. [CIKM 2022] **Contrastive Knowledge Graph Error Detection** [[paper]](https:\u002F\u002Fwww4.comp.polyu.edu.hk\u002F~xiaohuang\u002Fdocs\u002FQinggang_CIKM2022.pdf)\n 1. [TKDE 2022] **Disentangled Graph Contrastive Learning With Independence Promotion** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9893319)\n 1. [ECML-PKDD 2022] **Supervised Graph Contrastive Learning for Few-shot Node Classification** [[paper]](https:\u002F\u002F2022.ecmlpkdd.org\u002Fwp-content\u002Fuploads\u002F2022\u002F09\u002Fsub_764.pdf)\n 1. [Information Sciences 2022] **Graph Prototypical Contrastive Learning** [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS002002552201057X)\n 1. [ICAAN 2022] **Knowledge-Aware Self-supervised Graph Representation Learning for Recommendation** [[paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-15937-4_35)\n 1. [arXiv 2022] **Self-supervised Representation Learning on Electronic Health Records with Graph Kernel Infomax** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2209.00655.pdf)\n 1. [arXiv 2022] **Disentangled Graph Contrastive Learning for Review-based Recommendation** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2209.01524.pdf)\n 1. [arXiv 2022] **Contrastive Learning with Heterogeneous Graph Attention Networks on Short Text Classification** [[paper]](https:\u002F\u002Fdro.dur.ac.uk\u002F36856\u002F1\u002F36856.pdf)\n 1. [arXiv 2022] **Features Based Adaptive Augmentation for Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fftp\u002Farxiv\u002Fpapers\u002F2207\u002F2207.01792.pdf)\n 1. [TKDE 2022] **GCCAD: Graph Contrastive Learning for Anomaly Detection** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9870034)\n 1. [JCIM 2022] **SMICLR: Contrastive Learning on Multiple Molecular Representations for Semisupervised and Unsupervised Representation Learning** [[paper]](https:\u002F\u002Fpubs.acs.org\u002Fdoi\u002Ffull\u002F10.1021\u002Facs.jcim.2c00521)\n 1. [arXiv 2022] **XSimGCL: Towards Extremely Simple Graph Contrastive Learning for Recommendation** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.02544)[[code]](https:\u002F\u002Fgithub.com\u002FCoder-Yu\u002FSELFRec)\n 1. [CIKM 2022] **Relational Self-Supervised Learning on Graphs** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2208.10493.pdf)[[code]](https:\u002F\u002Fgithub.com\u002FNamkyeong\u002FRGRL)\n 1. [Information Sciences 2022] **Self-Supervised Graph Representation Learning via Positive Mining** [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0020025522009495)\n 1. [arXiv 2022] **Heterogeneous Graph Masked Autoencoders** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2208.09957.pdf)\n 1. [arXiv 2022] **KRACL: Contrastive Learning with Graph Context Modeling for Sparse Knowledge Graph Completion** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2208.07622.pdf)\n 1. [arXiv 2022] **R\\'enyiCL: Contrastive Representation Learning with Skew R\\'enyi Divergence** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2208.06270.pdf)\n 1. [TNNLS 2022] **Prototypical Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2106.09645.pdf)\n 1. [KDD 2022] **Mining Spatio-Temporal Relations via Self-Paced Graph Contrastive Learning** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3534678.3539422)\n 1. [KDD 2022] **Rep2Vec: Repository Embedding via Heterogeneous Graph Adversarial Contrastive Learning** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3534678.3539324)\n 1. [arXiv 2022] **Deep Contrastive Multiview Network Embedding** [[paper]](https:\u002F\u002Fsxkdz.github.io\u002Ffiles\u002Fpublications\u002FCIKM\u002FCREME\u002FCREME.pdf)\n 1. [arXiv 2022] **Analyzing Data-Centric Properties for Contrastive Learning on Graphs** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2208.02810.pdf)\n 1. [KDD 2022] **Mask and Reason: Pre-Training Knowledge Graph Transformers for Complex Logical Queries** [[paper]](https:\u002F\u002Fkeg.cs.tsinghua.edu.cn\u002Fjietang\u002Fpublications\u002FKDD22-Liu-et-al-KG-Transformer.pdf)\n 1. [arXiv 2022] **Generative Subgraph Contrast for Self-Supervised Graph Representation Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2207.11996.pdf)\n 1. [IJCAI 2022] **Graph Masked Autoencoder Enhanced Predictor for Neural Architecture Search** [[paper]](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F0432.pdf)\n 1. [IJCAI 2022] **Proximity Enhanced Graph Neural Networks with Channel Contrast** [[paper]](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F0340.pdf)\n 1. [IJCAI 2022] **Rethinking the Promotion Brought by Contrastive Learning to Semi-Supervised Node Classification** [[paper]](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F0395.pdf)\n 1. [IPM 2022] **HCNA: Hyperbolic Contrastive Learning Framework for Self-Supervised Network Alignment** [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS0306457322001315)\n 1. [arXiv 2022] **3D Equivariant Molecular Graph Pretraining** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2207.08824.pdf)\n 1. [arXiv 2022] **Unified 2D and 3D Pre-Training of Molecular Representations** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2207.08806.pdf)\n 1. [arXiv 2022] **Does GNN Pretraining Help Molecular Representation?** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2207.06010.pdf)\n 1. [arXiv 2022] **Latent Augmentation For Better Graph Self-Supervised Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2206.12933.pdf)\n 1. [arXiv 2022] **Geometry Contrastive Learning on Heterogeneous Graphs** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2206.12547.pdf)\n 1. [KIS 2022] **Self-supervised role learning for graph neural networks** [[paper]](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10115-022-01694-5)\n 1. [JFCST 2022] **Graph Neural Network Defense Combined with Contrastive Learning** [[paper]](http:\u002F\u002Ffcst.ceaj.org\u002FEN\u002Farticle\u002FdownloadArticleFile.do?attachType=PDF&id=3113)\n 1. [ICMLW 2022] **Evaluating Self-Supervised Learned Molecular Graphs** [[paper]](https:\u002F\u002Fopenreview.net\u002Fpdf?id=LeJC_Mf5rx-)\n 1. [KDD 2022] **Reliable Representations Make A Stronger Defender: Unsupervised Structure Refinement for Robust GNN** [[paper]](https:\u002F\u002Fponderly.github.io\u002Fpub\u002FSTABLE_KDD2022.pdf)\n 1. [ICMLW 2022] **Featurizations Matter: A Multiview Contrastive Learning Approach to Molecular Pretraining** [[paper]](https:\u002F\u002Fopenreview.net\u002Fpdf?id=Pm1Q1X3avx1)\n 1. [bioRiv 2022] **Cross-modal Graph Contrastive Learning with Cellular Images** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002Fbiorxiv\u002Fearly\u002F2022\u002F06\u002F06\u002F2022.06.05.494905.full.pdf)\n 1. [Information Sciences 2022] **A new self-supervised task on graphs: Geodesic distance prediction** [[paper]]([https:\u002F\u002Fhansen7.github.io\u002Fsandbox\u002Fmolgrapheval.pdf](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS0020025522006375))\n 1. [arXiv 2022] **Evaluating Self-Supervised Learning for Molecular Graph Embeddings** [[paper]](https:\u002F\u002Fhansen7.github.io\u002Fsandbox\u002Fmolgrapheval.pdf)\n 1. [arXiv 2022] **Evaluating Graph Generative Models with Contrastively Learned Features** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2206.06234.pdf)\n 1. [arXiv 2022] **COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2206.04726.pdf)\n 1. [arXiv 2022] **Decoupled Self-supervised Learning for Non-Homophilous Graphs** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2206.03601.pdf)\n 1. [arXiv 2022] **Interpolation-based Correlation Reduction Network for Semi-Supervised Graph Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2206.02796.pdf)\n 1. [arXiv 2022] **Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2206.01535.pdf)\n 1. [arXiv 2022] **KPGT: Knowledge-Guided Pre-training of Graph Transformer for Molecular Property Prediction** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2206.03364.pdf)\n 1. [CVPR 2022] **Robust Optimization As Data Augmentation for Large-Scale Graphs** [[paper]](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2022\u002Fpapers\u002FKong_Robust_Optimization_As_Data_Augmentation_for_Large-Scale_Graphs_CVPR_2022_paper.pdf)\n 1. [arXiv 2022] **COIN: Co-Cluster Infomax for Bipartite Graphs** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2206.00006.pdf)\n 3. [TSIPN 2022] **Fair Contrastive Learning on Graphs** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9779533)\n 4. [arXiv 2022] **I’m Me, We’re Us, and I’m Us: Tri-directional Contrastive Learning on Hypergraphs** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2206.04739.pdf)\n 5. [TNNLS 2022] **CLEAR: Cluster-Enhanced Contrast for Self-Supervised Graph Representation Learning** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9791433)\n 6. [arXiv 2022] **Let Invariant Rationale Discovery Inspire Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2206.07869.pdf)\n 7. [arXiv 2022] **Omni-Granular Ego-Semantic Propagation for Self-Supervised Graph Representation Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2205.15746.pdf)\n 8. [arXiv 2022] **Improving Subgraph Representation Learning via Multi-View Augmentation** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2205.13038.pdf)\n 9. [arXiv 2022] **Triangular Contrastive Learning on Molecular Graphs** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2205.13279.pdf)\n 10. [KDD 2022] **GraphMAE: Self-supervised Masked Graph Autoencoders** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2205.10803.pdf)\n 11. [arXiv 2022] **MaskGAE: Masked Graph Modeling Meets Graph Autoencoders** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2205.10053.pdf)\n 12. [ICML 2022] **Understanding Limitations of Unsupervised Graph Representation Learning from a Data-Dependent Perspective** [[paper]](https:\u002F\u002Fwww.osti.gov\u002Fservlets\u002Fpurl\u002F1868861)\n 13. [arXiv 2022] **Towards Explanation for Unsupervised Graph-Level Representation Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2205.09934.pdf)\n 14. [arXiv 2022] **ImGCL: Revisiting Graph Contrastive Learning on Imbalanced Node Classification** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2205.11332.pdf)\n 15. [TNNLS 2022] **Collaborative Decision-Reinforced Self-Supervision for Attributed Graph Clustering** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9777842)\n 16. [arXiv 2022] **Contrastive Graph Learning with Graph Convolutional Networks** [[paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-031-06555-2_7)\n 17. [TISPN 2022] **Fair Contrastive Learning on Graphs** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9779533)\n 18. [arXiv 2022] **SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks** [[paper]](https:\u002F\u002Fzepengzhang.com\u002FNotes\u002F2022\u002F20220507.pdf)\n 19. [arXiv 2022] **HCL: Hybrid Contrastive Learning for Graph-based Recommendation** [[paper]](https:\u002F\u002Fassets.amazon.science\u002F21\u002F8b\u002Fa804e89041f1a83bb1f77fa6aaee\u002Fhcl-hybrid-contrastive-learning-for-graph-based-recommendation.pdf)\n 20. [arXiv 2022] **Representation learning with function call graph transformations for malware open set recognition** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2205.07865.pdf)\n 21. [arXiv 2022] **Simple Contrastive Graph Clustering** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2205.07865.pdf)\n 22. [NCA 2022] **Self-supervised graph representation learning using multi-scale subgraph views contrast** [[paper]](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs00521-022-07299-x)\n 23. [ACL 2022] **JointCL: A Joint Contrastive Learning Framework for Zero-Shot Stance Detection** [[paper]](https:\u002F\u002Faclanthology.org\u002F2022.acl-long.7\u002F) \n 24. [IPM 2022] **Contrastive Graph Convolutional Networks with adaptive augmentation for text classification** [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS0306457322000681) \n 25. [PAKDD 2022] **Contrastive Attributed Network Anomaly Detection with Data Augmentation** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1007\u002F978-3-031-05936-0_35) \n 26. [DASFAA 2022] **CSGNN: Improving Graph Neural Networks with Contrastive Semi-supervised Learning** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1007\u002F978-3-031-00123-9_58)\n 27. [arXiv 2022] **Dynamic Graph Representation Based on Temporal and Contextual Contrasting** [[paper]](https:\u002F\u002Fassets.researchsquare.com\u002Ffiles\u002Frs-1588877\u002Fv1_covered.pdf?c=1651680782)\n 28. [DASFAA 2022] **Diffusion-Based Graph Contrastive Learning for Recommendation with Implicit Feedback** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1007\u002F978-3-031-00126-0_15)\n 29. [arXiv 2022] **FastGCL: Fast Self-Supervised Learning on Graphs via Contrastive Neighborhood Aggregation** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2205.00905.pdf)\n 30. [arXiv 2022] **RoSA: A Robust Self-Aligned Framework for Node-Node Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2204.13846.pdf)\n 31. [arXiv 2022] **Heterogeneous Graph Neural Networks using Self-supervised Reciprocally Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2205.00256.pdf)\n 32. [WSDM 2022] **JGCL: Joint Self-Supervised and Supervised Graph Contrastive Learning** [[paper]](https:\u002F\u002Fwww2022.thewebconf.org\u002FPaperFiles\u002F161.pdf)\n 33. [AAAI 2022] **SAIL: Self-Augmented Graph Contrastive Learning** [[paper]](https:\u002F\u002Fwww.aaai.org\u002FAAAI22Papers\u002FAAAI-8378.YuL.pdf)\n 34. [ICASSP 2022] **Graph Fine-Grained Contrastive Representation Learning** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9746085)\n 35. [arXiv 2022] **SCGC: Self-Supervised Contrastive Graph Clustering** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2204.12656.pdf)\n 36. [arXiv 2022] **A Content-First Benchmark for Self-Supervised Graph Representation Learning** [[paper]](https:\u002F\u002Fgraph-learning-benchmarks.github.io\u002Fassets\u002Fpapers\u002Fglb2022\u002FA_Content_First_Benchmark_for_Self_Supervised_Graph_Representation_Learning.pdf)\n 37. [SIGIR 2022] **Hypergraph Contrastive Collaborative Filtering** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2204.12200.pdf)\n 38. [WWW 2022] **Rumor Detection on Social Media with Graph Adversarial Contrastive Learning** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3485447.3511999)\n 39. [arXiv 2022] **A Review-aware Graph Contrastive Learning Framework for Recommendation** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2204.12063.pdf)\n 40. [WWW 2022] **Robust Self-Supervised Structural Graph Neural Network for Social Network Prediction** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3485447.3512182)\n 41. [arXiv 2022] **CGC: Contrastive Graph Clustering for Community Detection and Tracking** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2204.08504.pdf)\n 42. [TCyber 2022] **Multiview Deep Graph Infomax to Achieve Unsupervised Graph Embedding** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9758652)\n 43. [arXiv 2022] **MVGCNMDA: Multi-view Graph Augmentation Convolutional Network for Uncovering Disease-Related Microbes** [[paper]](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs12539-022-00514-2)\n 44. [arXiv 2022] **CERES: Pretraining of Graph-Conditioned Transformer for Semi-Structured Session Data** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2204.04303.pdf)\n 45. [arXiv 2022] **Self-Supervised Graph Neural Network for Multi-Source Domain Adaptation** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2204.05104.pdf)\n 46. [SIGIR 2022] **Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2112.08679)[[code]](https:\u002F\u002Fgithub.com\u002FCoder-Yu\u002FSELFRec)\n 47. [arXiv 2022] **Explanation Graph Generation via Pre-trained Language Models: An Empirical Study with Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2204.04813)\n 48. [arXiv 2022] **Augmentation-Free Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2204.04874)\n 49. [TCybern 2022] **Link-Information Augmented Twin Autoencoders for Network Denoising** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9745753)\n 50. [arXiv 2022] **Node Representation Learning in Graph via Node-to-Neighbourhood Mutual Information Maximization** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.12265)\n 51. [arXiv 2022] **GraphCoCo: Graph Complementary Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.12821)\n 52. [arXiv 2022] **Unsupervised Heterophilous Network Embedding via r-Ego Network Discrimination** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.10866.pdf)\n 53. [Bioinformatics 2022] **Supervised Graph Co-contrastive Learning for Drug-Target Interaction Prediction** [[paper]](https:\u002F\u002Facademic.oup.com\u002Fbioinformatics\u002Fadvance-article\u002Fdoi\u002F10.1093\u002Fbioinformatics\u002Fbtac164\u002F6551245?login=true)\n 54. [arXiv 2022] **Supervised Contrastive Learning with Structure Inference for Graph Classification** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.07691)\n 55. [arXiv 2022] **Defending Graph Convolutional Networks against Dynamic Graph Perturbations via Bayesian Self-supervision** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.03762.pdf)\n 57. [arXiv 2022] **Analyzing Heterogeneous Networks with Missing Attributes by Unsupervised Contrastive Learning** [[paper]](https:\u002F\u002Fyangliang.github.io\u002Fpdf\u002Ftnnls22.pdf)\n 58. [arXiv 2022] **Improving Molecular Contrastive Learning via Faulty Negative Mitigation and Decomposed Fragment Contrast** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.09346.pdf)\n 59. [arXiv 2022] **Contrastive Meta Learning with Behavior Multiplicity for Recommendation** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.08523.pdf)[[code]](https:\u002F\u002Fgithub.com\u002Fweiwei1206\u002FCML.git)\n 60. [arXiv 2022] **Fair Node Representation Learning via Adaptive Data Augmentation** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2201.08549.pdf)\n 61. [arXiv 2022] **Learning Graph Augmentations to Learn Graph Representations** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2201.09830.pdf)[[code]](https:\u002F\u002Fgithub.com\u002Fkavehhassani\u002Flg2ar)\n 62. [arXiv 2022] **Graph Data Augmentation for Graph Machine Learning: A Survey** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.08871.pdf)\n 63. [arXiv 2022] **Data Augmentation for Deep Graph Learning: A Survey** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2202.08235)\n 64. [arXiv 2022] **Adversarial Graph Contrastive Learning with Information Regularization** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.06491.pdf)\n 65. [arXiv 2022] **SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.03104.pdf)\n 66. [NeurIPS 2022] **Graph Self-supervised Learning with Accurate Discrepancy Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.02989.pdf)\n 67. [arXiv 2022] **Learning Robust Representation through Graph Adversarial Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2201.13025.pdf)\n 68. [arXiv 2022] **Self-supervised Graphs for Audio Representation Learning with Limited Labeled Data** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.00097.pdf)\n 69. [arXiv 2022] **Link Prediction with Contextualized Self-Supervision** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2201.10069.pdf)\n 70. [arXiv 2022] **Dual Space Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2201.07409.pdf)\n 71. [arXiv 2022] **Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagation** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2201.07986.pdf)\n 72. [arXiv 2022] **From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale Contrastive Learning Approach** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.05525)\n 73. [arXiv 2022] **Dual Space Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2201.07409)\n 74. [arXiv 2022] **Structure-Enhanced Heterogeneous Graph Contrastive Learning** [[paper]](https:\u002F\u002Fsxkdz.github.io\u002Ffiles\u002Fpublications\u002FSDM\u002FSTENCIL\u002FSTENCIL.pdf)\n 75. [bioRxiv 2022] **Towards Effective and Generalizable Fine-tuning for Pre-trained Molecular Graph Models** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002Fbiorxiv\u002Fearly\u002F2022\u002F02\u002F06\u002F2022.02.03.479055.full.pdf)\n 76. [SDM 2022] **Neural Graph Matching for Pre-training Graph Neural Networks** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.01597.pdf) [[code]](https:\u002F\u002Fgithub.com\u002FRUCAIBox\u002FGMPT)\n 77. [TNNLS 2022] **Analyzing Heterogeneous Networks with Missing Attributes by Unsupervised Contrastive Learning** [[paper]](https:\u002F\u002Fyangliang.github.io\u002Fpdf\u002Ftnnls22.pdf)\n 78. [WWW 2022] **Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.06200.pdf) [[code]](https:\u002F\u002Fgithub.com\u002FRUCAIBox\u002FNCL)\n 79. [WWW 2022] **ClusterSCL: Cluster-Aware Supervised Contrastive Learning on Graphs** [[paper]](https:\u002F\u002Fxiaojingzi.github.io\u002Fpublications\u002FWWW22-Wang-et-al-ClusterSCL.pdf)\n 80. [ICLR 2022] **Large-Scale Representation Learning on Graphs via Bootstrapping** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2102.06514.pdf)[[Code]](https:\u002F\u002Fgithub.com\u002FNamkyeong\u002FBGRL_Pytorch)\n 81. [ICLR 2022] **Automated Self-Supervised Learning for Graphs** [[paper]](https:\u002F\u002Fopenreview.net\u002Fforum?id=rFbR4Fv-D6-) [[code]](https:\u002F\u002Fgithub.com\u002FChandlerBang\u002FAutoSSL)\n 82. [AAAI 2022] **Self-supervised Graph Neural Networks via Diverse and Interactive Message Passing** [[paper]](https:\u002F\u002Fyangliang.github.io\u002Fpdf\u002Faaai22.pdf)\n 83. [AAAI 2022] **Augmentation-Free Self-Supervised Learning on Graphs** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2112.02472.pdf)[[code]](https:\u002F\u002Fgithub.com\u002FNamkyeong\u002FAFGRL)\n 84. [AAAI 2022] **Molecular Contrastive Learning with Chemical Element Knowledge Graph** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2112.00544.pdf)\n 85. [AAAI 2022] **Deep Graph Clustering via Dual Correlation Reduction** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2112.14772)[[code]](https:\u002F\u002Fgithub.com\u002Fyueliu1999\u002FDCRN)\n 86. [AAAI 2022] **Simple Unsupervised Graph Representation Learning** [[paper]](https:\u002F\u002Fwww.aaai.org\u002FAAAI22Papers\u002FAAAI-3999.MoY.pdf)\n 87. [WSDM 2022] **Bringing Your Own View: Graph Contrastive Learning without Prefabricated Data Augmentations** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2201.01702) [[code]](https:\u002F\u002Fgithub.com\u002FShen-Lab\u002FGraphCL_Automated)\n 88. [ICOIN 2022] **Adaptive Self-Supervised Graph Representation Learning** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9687176)\n 89. [NPL 2022] **How Does Bayesian Noisy Self-Supervision Defend Graph Convolutional Networks?** [[paper]](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11063-022-10750-8)\n 90. [SIGIR 2022] **Knowledge Graph Contrastive Learning for Recommendation** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2205.00976) [[code]](https:\u002F\u002Fgithub.com\u002Fyuh-yang\u002FKGCL-SIGIR22)\n \n ## Year 2021\n 1. [AAAI 2021] **Self-supervised hypergraph convolutional networks for session-based recommendation** [[paper]](https:\u002F\u002Fwww.aaai.org\u002FAAAI21Papers\u002FAAAI-1889.XiaX.pdf)\n 1. [arXiv 2021] **Pre-training Graph Neural Network for Cross Domain Recommendation** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2111.08268.pdf)\n 17. [arXiv 2021] **Augmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practices** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2111.03220.pdf)\n 18. [arXiv 2021] **Collaborative Graph Contrastive Learning: Data Augmentation Composition May Not be Necessary for Graph Representation Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2111.03262.pdf)\n 13. [arXiv 2021] **Multi-task Self-distillation for Graph-based Semi-Supervised Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2112.01174.pdf)\n 14. [arXiv 2021] **Subgraph Contrastive Link Representation Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2112.01165.pdf)\n 3. [arXiv 2021] **Multilayer Graph Contrastive Clustering Network** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2112.14021.pdf)\n 3. [arXiv 2021] **Graph Representation Learning via Contrasting Cluster Assignments** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2112.07934.pdf)\n 3. [arXiv 2021] **Graph-wise Common Latent Factor Extraction for Unsupervised Graph Representation Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2112.08830.pdf)\n 3. [arXiv 2021] **Bayesian Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2112.07823.pdf)\n 3. [arXiv 2021] **TCGL: Temporal Contrastive Graph for Self-supervised Video Representation Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2112.03587.pdf)\n 26. [arXiv 2021] **Graph Communal Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2110.14863.pdf)\n 27. [arXiv 2021] **Self-supervised Contrastive Attributed Graph Clustering** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2110.08264.pdf)\n 28. [arXiv 2021] **Self-Supervised Learning for Molecular Property Prediction** [[paper]](https:\u002F\u002Fchemrxiv.org\u002Fengage\u002Fapi-gateway\u002Fchemrxiv\u002Fassets\u002Forp\u002Fresource\u002Fitem\u002F61677becaa918db6bf2a31cb\u002Foriginal\u002Fself-supervised-learning-for-molecular-property-prediction.pdf)\n 29. [arXiv 2021] **RPT: Toward Transferable Model on Heterogeneous Researcher Data via Pre-Training** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2110.07336.pdf)\n 30. [arXiv 2021] **Scalable Consistency Training for Graph Neural Networks via Self-Ensemble Self-Distillation** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2110.06290.pdf)\n 31. [arXiv 2021] **PRE-TRAINING MOLECULAR GRAPH REPRESENTATION WITH 3D GEOMETRY** [[paper]](https:\u002F\u002Fwyliu.com\u002Fpapers\u002FGraphMVP.pdf) [[code]](https:\u002F\u002Fgithub.com\u002Fchao1224\u002FGraphMVP)\n 32. [arXiv 2021] **3D Infomax improves GNNs for Molecular Property Prediction** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2110.04126v1) [[code]](https:\u002F\u002Fgithub.com\u002FHannesStark\u002F3DInfomax)\n 34. [arXiv 2021] **Motif-based Graph Self-Supervised Learning for Molecular Property Prediction** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2110.00987.pdf)\n 35. [arXiv 2021] **Debiased Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2110.02027.pdf)\n 36. [arXiv 2021] **3D-Transformer: Molecular Representation with Transformer in 3D Space** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2110.01191.pdf)\n 37. [arXiv 2021] **Contrastive Pre-Training of GNNs on Heterogeneous Graphs** [[paper]](https:\u002F\u002Fyuanfulu.github.io\u002Fpublication\u002FCIKM-CPT.pdf)\n 38. [arXiv 2021] **Contrastive Graph Convolutional Networks for Hardware Trojan Detection in Third Party IP Cores** [[paper]](https:\u002F\u002Fpeople.cs.vt.edu\u002F~ramakris\u002Fpapers\u002FHardware_Trojan_Trigger_Detection__HOST2021.pdf)\n 39. [arXiv 2021] **GeomGCL: Geometric Graph Contrastive Learning for Molecular Property Prediction** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2109.11730.pdf)\n 40. [arXiv 2021] **Adaptive Multi-layer Contrastive Graph Neural Networks** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2109.14159.pdf)\n 42. [arXiv 2021] **Graph-MVP: Multi-View Prototypical Contrastive Learning for Multiplex Graphs** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2109.03560.pdf)\n 43. [arXiv 2021] **Hyper Meta-Path Contrastive Learning for Multi-Behavior Recommendation** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2109.02859.pdf)\n 44. [arXiv 2021] **Negative Sampling Strategies for Contrastive Self-Supervised Learning of Graph Representations** [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS0165168421003479)\n 45. [arXiv 2021] **Structure-Aware Hard Negative Mining for Heterogeneous Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2108.13886.pdf)\n 46. [arXiv 2021] **Spatio-Temporal Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2108.11873.pdf)\n 47. [arXiv 2021] **Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2108.09896.pdf)\n 92. [Arxiv 2021] **Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2101.06448) [[code]](https:\u002F\u002Fgithub.com\u002FCoder-Yu\u002FRecQ)\n 53. [arXiv 2021] **GCCAD: Graph Contrastive Coding for Anomaly Detection** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2108.07516.pdf)\n 54. [arXiv 2021] **Contrastive Self-supervised Sequential Recommendation with Robust Augmentation** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2108.06479.pdf)\n 55. [arXiv 2021] **RRLFSOR: An Efficient Self-Supervised Learning Strategy of Graph Convolutional Networks** [[paper]](https:\u002F\u002Farxiv.org\u002Fftp\u002Farxiv\u002Fpapers\u002F2108\u002F2108.07481.pdf)\n 59. [arXiv 2021] **Group Contrastive Self-Supervised Learning on Graphs** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2107.09787) \n 60. [arXiv 2021] **Multi-Level Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2107.02639)\n 62. [arXiv 2021] **From Canonical Correlation Analysis to Self-supervised Graph Neural Networks** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.12484) [[code]](https:\u002F\u002Fgithub.com\u002Fhengruizhang98\u002FCCA-SSG)\n 63. [arXiv 2021] **Evaluating Modules in Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.08171) [[code]](https:\u002F\u002Fgithub.com\u002Fthunlp\u002FOpenGCL)\n 70. [arXiv 2021] **Prototypical Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2106.09645.pdf)\n 71. [arXiv 2021] **Fairness-Aware Node Representation Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2106.05391.pdf)\n 72. [arXiv 2021] **Adversarial Graph Augmentation to Improve Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.05819)\n 73. [arXiv 2021] **Graph Barlow Twins: A self-supervised representation learning framework for graphs** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2106.02466.pdf)\n 74. [arXiv 2021] **Self-Supervised Graph Learning with Proximity-based Views and Channel Contrast** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2106.03723.pdf)\n 75. [arXiv 2021] **Self-supervised on Graphs: Contrastive, Generative,or Predictive** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2105.07342)\n 76. [arXiv 2021] **FedGL: Federated Graph Learning Framework with Global Self-Supervision** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2105.03170.pdf)\n 78. [arXiv 2021] **Hop-Count Based Self-Supervised Anomaly Detection on Attributed Networks** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2104.07917)\n 79. [arXiv 2021] **Representation Learning for Networks in Biology and Medicine: Advancements, Challenges, and Opportunities** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2104.04883)\n 80. [arXiv 2021] **Graph Representation Learning by Ensemble Aggregating Subgraphs via Mutual Information Maximization** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.13125)\n 81. [arXiv 2021] **Drug Target Prediction Using Graph Representation Learning via Substructures Contrast** [[paper]](https:\u002F\u002Fwww.preprints.org\u002Fmanuscript\u002F202103.0337\u002Fv1)\n 82. [arXiv 2021] **Self-supervised Auxiliary Learning for Graph Neural Networks via Meta-Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.00771)\n 83. [arXiv 2021] **Graph Self-Supervised Learning: A Survey** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.00111)\n 84. [arXiv 2021] **Towards Robust Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2102.13085.pdf)\n 85. [arXiv 2021] **Pre-Training on Dynamic Graph Neural Networks** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.12380)\n 86. [arXiv 2021] **Self-Supervised Learning of Graph Neural Networks: A Unified Review** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.10757)\n 61. [Openreview 2021] **An Empirical Study of Graph Contrastive Learning** [[paper]](https:\u002F\u002Fopenreview.net\u002Fforum?id=fYxEnpY-__G)\n 1. [BIBM 2021] **SGAT: a Self-supervised Graph Attention Network for Biomedical Relation Extraction** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9669699)\n 95. [BIBM 2021] **Molecular Graph Contrastive Learning with Parameterized Explainable Augmentations** [[paper]](https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002F10.1101\u002F2021.12.03.471150v1)\n 5. [NeurIPS 2021 Workshop] **Self-Supervised GNN that Jointly Learns to Augment** [[paper]](https:\u002F\u002Fwww.researchgate.net\u002Fprofile\u002FZekarias-Kefato\u002Fpublication\u002F356997993_Self-Supervised_GNN_that_Jointly_Learns_to_Augment\u002Flinks\u002F61b75d88a6251b553ab64ff4\u002FSelf-Supervised-GNN-that-Jointly-Learns-to-Augment.pdf)\n 5. [NeurIPS 2021 Workshop] **Contrastive Embedding of Structured Space for Bayesian Optimisation** [[paper]](https:\u002F\u002Fopenreview.net\u002Fpdf?id=xFpkJUMS9te)\n 5. [NeurIPS 2021] **Enhancing Hyperbolic Graph Embeddings via Contrastive Learning** [[paper]](https:\u002F\u002Fsslneurips21.github.io\u002Ffiles\u002FCameraReady\u002FNeurIPS_2021_workshop_version2.pdf)\n 5. [NeurIPS 2021] **Graph Adversarial Self-Supervised Learning** [[paper]](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2021\u002Ffile\u002F7d3010c11d08cf990b7614d2c2ca9098-Paper.pdf)\n 6. [NeurIPS 2021] **Contrastive laplacian eigenmaps** [[paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2021\u002Ffile\u002F2d1b2a5ff364606ff041650887723470-Paper.pdf)\n 7. [NeurIPS 2021] **Directed Graph Contrastive Learning** [[paper]](https:\u002F\u002Fzekuntong.com\u002Ffiles\u002Fdigcl_nips.pdf)[[code]](https:\u002F\u002Fgithub.com\u002Fflyingtango\u002FDiGCL)\n 8. [NeurIPS 2021] **Multi-view Contrastive Graph Clustering** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2110.11842.pdf)[[code]](https:\u002F\u002Fgithub.com\u002FPanern\u002FMCGC)\n 9. [NeurIPS 2021] **From Canonical Correlation Analysis to Self-supervised Graph Neural Networks** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2106.12484.pdf)[[code]](https:\u002F\u002Fgithub.com\u002Fhengruizhang98\u002FCCA-SSG)\n 10. [NeurIPS 2021] **InfoGCL: Information-Aware Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2110.15438.pdf)\n 11. [NeurIPS 2021] **Adversarial Graph Augmentation to Improve Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.05819)[[code]](https:\u002F\u002Fgithub.com\u002Fsusheels\u002Fadgcl)\n 12. [NeurIPS 2021] **Disentangled Contrastive Learning on Graphs** [[paper]](https:\u002F\u002Fopenreview.net\u002Fpdf?id=C_L0Xw_Qf8M)\n 20. [CIKM 2021] **Multimodal Graph Meta Contrastive Learning** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3459637.3482151)\n 21. [CIKM 2021] **Self-supervised Representation Learning on Dynamic Graphs** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3459637.3482389)\n 22. [CIKM 2021] **Rectifying Pseudo Labels: Iterative Feature Clustering for Graph Representation Learning** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3459637.3482469)\n 23. [CIKM 2021] **SGCL: Contrastive Representation Learning for Signed Graphs** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3459637.3482478)\n 24. [CIKM 2021] **Semi-Supervised and Self-Supervised Classification with Multi-View Graph Neural Networks** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3459637.3482477)\n 25. [CIKM 2021] **Social Recommendation with Self-Supervised Metagraph Informax Network** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3459637.3482480) [[code]](https:\u002F\u002Fgithub.com\u002FSocialRecsys\u002FSMIN)\n 48. [IJCAI 2021] **Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning** [[paper]](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2021\u002F0204.pdf)\n 49. [IJCAI 2021] **Pairwise Half-graph Discrimination: A Simple Graph-level Self-supervised Strategy for Pre-training Graph Neural Networks** [[paper]](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2021\u002F0371.pdf)\n 50. [IJCAI 2021] **CuCo: Graph Representation with Curriculum Contrastive Learning** [[paper]](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2021\u002F0317.pdf)\n 51. [IJCAI 2021] **Graph Debiased Contrastive Learning with Joint Representation Clustering** [[paper]](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2021\u002F0473.pdf)\n 52. [IJCAI 2021] **CSGNN: Contrastive Self-Supervised Graph Neural Network for Molecular Interaction Prediction** [[paper]](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2021\u002F0517.pdf)\n 56. [KDD 2021] **MoCL: Data-driven Molecular Fingerprint via Knowledge-aware Contrastive Learning from Molecular Graph** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3447548.3467186) [[code]](https:\u002F\u002Fgithub.com\u002Fillidanlab\u002FMoCL-DK)\n 57. [KDD 2021] **Contrastive Multi-View Multiplex Network Embedding with Applications to Robust Network Alignment** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3447548.3467227)\n 58. [KDD 2021] **Adaptive Transfer Learning on Graph Neural Networks** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2107.08765.pdf)\n 64. :fire:[ICML 2021] **Graph Contrastive Learning Automated** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.07594) [[code]](https:\u002F\u002Fgithub.com\u002FShen-Lab\u002FGraphCL_Automated)\n 66. [ICML 2021] **Self-supervised Graph-level Representation Learning with Local and Global Structure** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2106.04113) [[code]](https:\u002F\u002Fgithub.com\u002FDeepGraphLearning\u002FGraphLoG)\n 67. [KDD 2021] **Pre-training on Large-Scale Heterogeneous Graph** [[paper]](http:\u002F\u002Fwww.shichuan.org\u002Fdoc\u002F111.pdf)\n 68. [KDD 2021] **MoCL: Contrastive Learning on Molecular Graphs with Multi-level Domain Knowledge** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2106.04509.pdf)\n 69. [KDD 2021] **Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2105.09111) [[code]](https:\u002F\u002Fgithub.com\u002Fliun-online\u002FHeCo)\n 87. [WWW 2021 Workshop] **Iterative Graph Self-Distillation** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.12609)\n 88. [WWW 2021] **HDMI: High-order Deep Multiplex Infomax** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.07810) [[code]](https:\u002F\u002Fgithub.com\u002Fbaoyujing\u002FHDMI)\n 89. :fire:[WWW 2021] **Graph Contrastive Learning with Adaptive Augmentation** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.14945) [[code]](https:\u002F\u002Fgithub.com\u002FCRIPAC-DIG\u002FGCA)\n 90. [WWW 2021] **SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanism** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2101.08170) [[code]](https:\u002F\u002Fgithub.com\u002FRingBDStack\u002FSUGAR)\n 91. [WWW 2021] **Multi-view Graph Contrastive Representation Learning for Drug-Drug Interaction Prediction** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.11711) [[code]](https:\u002F\u002Fgithub.com\u002Fisjakewong\u002FMIRACLE)\n 93. :fire:[ICLR 2021] **How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision** [[paper]](https:\u002F\u002Fopenreview.net\u002Fforum?id=Wi5KUNlqWty) [[code]](https:\u002F\u002Fgithub.com\u002Fdongkwan-kim\u002FSuperGAT)\n 94. [WSDM 2021] **Pre-Training Graph Neural Networks for Cold-Start Users and Items Representation** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.07064) [[code]](https:\u002F\u002Fgithub.com\u002Fjerryhao66\u002FPretrain-Recsys)\n 41. [KBS 2021] **Multi-aspect self-supervised learning for heterogeneous information network** [[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS095070512100736X)\n 33. [CVPR 2021] **Zero-Shot Learning via Contrastive Learning on Dual Knowledge Graphs** [[paper]](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FICCV2021W\u002FGSP-CV\u002Fpapers\u002FWang_Zero-Shot_Learning_via_Contrastive_Learning_on_Dual_Knowledge_Graphs_ICCVW_2021_paper.pdf)\n 2. [ICBD 2021] **Session-based Recommendation via Contrastive Learning on Heterogeneous Graph** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9671296)\n 4. [ICONIP 2021] **Concordant Contrastive Learning for Semi-supervised Node Classification on Graph** [[paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-030-92185-9_48)\n 15. [ICCSNT 2021] **Graph Data Augmentation based on Adaptive Graph Convolution for Skeleton-based Action Recognition** [[paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9615451)\n 77. [IJCNN 2021] **Node Embedding using Mutual Information and Self-Supervision based Bi-level Aggregation** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2104.13014v1)\n \n ## Year 2020\n 1. [Openreview 2020] **Motif-Driven Contrastive Learning of Graph Representations** [[paper]](https:\u002F\u002Fopenreview.net\u002Fforum?id=qcKh_Msv1GP)\n 15. [Openreview 2020] **SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks** [[paper]](https:\u002F\u002Fopenreview.net\u002Fforum?id=a5KvtsZ14ev)\n 16. [Openreview 2020] **TopoTER: Unsupervised Learning of Topology Transformation Equivariant Representations** [[paper]](https:\u002F\u002Fopenreview.net\u002Fforum?id=9az9VKjOx00)\n 17. [Openreview 2020] **Graph-Based Neural Network Models with Multiple Self-Supervised Auxiliary Tasks** [[paper]](https:\u002F\u002Fopenreview.net\u002Fforum?id=hnJSgY7p33a)\n 19. [Openreview 2020] **Transfer Learning of Graph Neural Networks with Ego-graph Information Maximization** [[paper]](https:\u002F\u002Fopenreview.net\u002Fforum?id=J_pvI6ap5Mn)\n 1. [Arxiv 2020] **COAD: Contrastive Pre-training with Adversarial Fine-tuning for Zero-shot Expert Linking** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.11336) [[code]](https:\u002F\u002Fgithub.com\u002FBoChen-Daniel\u002FExpert-Linking)\n 12. [Arxiv 2020] **Distance-wise Graph Contrastive Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.07437)\n 23. :fire:[Arxiv 2020] **Self-supervised Learning on Graphs: Deep Insights and New Direction.** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.10141) [[code]](https:\u002F\u002Fgithub.com\u002FChandlerBang\u002FSelfTask-GNN)\n 24. :fire:[Arxiv 2020] **Deep Graph Contrastive Representation Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.04131)\n 29. [Arxiv 2020] **Self-supervised Training of Graph Convolutional Networks.** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.02380)\n 30. [Arxiv 2020] **Self-Supervised Graph Representation Learning via Global Context Prediction.** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2003.01604)\n 33. :fire:[Arxiv 2020] **Graph-Bert: Only Attention is Needed for Learning Graph Representations.** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2001.05140) [[code]](https:\u002F\u002Fgithub.com\u002Fanonymous-sourcecode\u002FGraph-Bert)\n 20. :fire:[NeurIPS 2020] **Self-Supervised Graph Transformer on Large-Scale Molecular Data** [[paper]](https:\u002F\u002Fdrug.ai.tencent.com\u002Fpublications\u002FGROVER.pdf)\n 21. [NeurIPS 2020] **Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.08294) [[code]](https:\u002F\u002Fgithub.com\u002Fmlvlab\u002FSELAR)\n 22. :fire:[NeurIPS 2020] **Graph Contrastive Learning with Augmentations** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.13902) [[code]](https:\u002F\u002Fgithub.com\u002FShen-Lab\u002FGraphCL)\n 25. :fire:[ICML 2020] **When Does Self-Supervision Help Graph Convolutional Networks?** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.09136) [[code]](https:\u002F\u002Fgithub.com\u002FShen-Lab\u002FSS-GCNs)\n 26. :fire:[ICML 2020] **Graph-based, Self-Supervised Program Repair from Diagnostic Feedback.** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.10636)\n 27. :fire:[ICML 2020] **Contrastive Multi-View Representation Learning on Graphs.** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.05582) [[code]](https:\u002F\u002Fgithub.com\u002Fkavehhassani\u002Fmvgrl)\n 28. [ICML 2020 Workshop] **Self-supervised edge features for improved Graph Neural Network training.** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.04777)\n 31. :fire:[KDD 2020] **GPT-GNN: Generative Pre-Training of Graph Neural Networks.** [[pdf]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.15437) [[code]](https:\u002F\u002Fgithub.com\u002Facbull\u002FGPT-GNN)\n 32. :fire:[KDD 2020] **GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training.** [[pdf]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.09963) [[code]](https:\u002F\u002Fgithub.com\u002FTHUDM\u002FGCC) \n 34. :fire:[ICLR 2020] **InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization.** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1908.01000) [[code]](https:\u002F\u002Fgithub.com\u002Ffanyun-sun\u002FInfoGraph)\n 35. :fire:[ICLR 2020] **Strategies for Pre-training Graph Neural Networks.** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1905.12265) [[code]](https:\u002F\u002Fgithub.com\u002Fsnap-stanford\u002Fpretrain-gnns)\n 36. :fire:[AAAI 2020] **Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labels.** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1902.11038)\n 1. [ICDM 2020] **Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learning** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.10273) [[code]](https:\u002F\u002Fgithub.com\u002Fyzjiao\u002FSubg-Con)\n \n ## Year 2019\n 1. [KDD 2019 Workshop] **SGR: Self-Supervised Spectral Graph Representation Learning.** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1811.06237)\n 1. [ICLR 2019 Workshop] **Can Graph Neural Networks Go \"Online\"? An Analysis of Pretraining and Inference.** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1905.06018)\n 1. [ICLR 2019 workshop] **Pre-Training Graph Neural Networks for Generic Structural Feature Extraction.** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1905.13728)\n 1. [Arxiv 2019] **Heterogeneous Deep Graph Infomax** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1911.08538) [[code]](https:\u002F\u002Fgithub.com\u002FYuxiangRen\u002FHeterogeneous-Deep-Graph-Infomax)\n 1. :fire:[ICLR 2019] **Deep Graph Informax.** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.10341) [[code]](https:\u002F\u002Fgithub.com\u002FPetarV-\u002FDGI)\n \n \n ## Other related papers\n  (implicitly using self-supersvied learning or applying graph neural networks in other domains)\n 1. [Arxiv 2020] **Self-supervised Learning: Generative or Contrastive.** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.08218)\n 1. [KDD 2020] **Octet: Online Catalog Taxonomy Enrichment with Self-Supervision.** [[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2006.10276.pdf)\n 1. [WWW 2020] **Structural Deep Clustering Network.** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3366423.3380214\n ) [[code]](https:\u002F\u002Fgithub.com\u002Fbdy9527\u002FSDCN)\n 1. [IJCAI 2019] **Pre-training of Graph Augmented Transformers for Medication Recommendation.** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1906.00346) [[code]](https:\u002F\u002Fgithub.com\u002Fjshang123\u002FG-Bert)\n 1. [AAAI 2020] **Unsupervised Attributed Multiplex Network Embedding** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1911.06750) [[code]](https:\u002F\u002Fgithub.com\u002Fpcy1302\u002FDMGI)\n 1. [WWW 2020] **Graph representation learning via graphical mutual information maximization** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F3366423.3380112)\n 1. [NeurIPS 2017] **Inductive Representation Learning on Large Graphs** [[paper]](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F2017\u002Fhash\u002F5dd9db5e033da9c6fb5ba83c7a7ebea9-Abstract.html) [[code]](https:\u002F\u002Fgithub.com\u002Fwilliamleif\u002FGraphSAGE)\n 1. [NeurIPS 2016 Workshop] **Variational Graph Auto-Encoders** [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1611.07308) [[code]](https:\u002F\u002Fgithub.com\u002Ftkipf\u002Fgae)\n 1. [WWW 2015] **LINE: Large-scale Information Network Embedding** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F2736277.2741093) [[code]](https:\u002F\u002Fgithub.com\u002Ftangjianpku\u002FLINE)\n 1. [KDD 2014] **DeepWalk: Online Learning of Social Representations** [[paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fabs\u002F10.1145\u002F2623330.2623732) [[code]](https:\u002F\u002Fgithub.com\u002Fphanein\u002Fdeepwalk)\n \n ## Acknowledgement\n \n This page is contributed and maintained by [Wei Jin](http:\u002F\u002Fcse.msu.edu\u002F~jinwei2\u002F)(joe.weijin@gmail.com), [Yuning You](https:\u002F\u002Fyyou1996.github.io\u002F)(yuning.you@tamu.edu) and [Yingheng Wang](https:\u002F\u002Fisjakewong.github.io\u002F)(jakewyh@163.com).","# awesome-self-supervised-gnn 快速上手指南\n\n`awesome-self-supervised-gnn` 并非一个可直接安装的软件包或框架，而是一个**精选论文与代码资源列表**。它汇集了图神经网络（GNN）领域中自监督学习方向的前沿研究成果。本指南将指导你如何利用该仓库查找资源、获取代码并复现相关算法。\n\n## 环境准备\n\n由于本仓库包含多个不同作者实现的独立项目，没有统一的前置依赖。你需要根据具体想复现的论文，配置相应的环境。\n\n### 系统要求\n- **操作系统**: Linux, macOS, 或 Windows (推荐 Linux)\n- **Python**: 建议版本 3.8+ (具体版本需参考目标论文的 `requirements.txt`)\n- **GPU**: 推荐配备 NVIDIA GPU 以加速训练 (可选，视具体模型而定)\n\n### 前置依赖\n在克隆仓库后，建议安装通用的深度学习基础库。大多数列表中的项目基于以下框架：\n- PyTorch\n- DGL (Deep Graph Library) 或 PyTorch Geometric (PyG)\n- NumPy, SciPy, Pandas\n\n## 安装步骤\n\n### 1. 克隆仓库\n首先，将资源列表仓库克隆到本地。国内用户推荐使用 Gitee 镜像（如有）或通过加速代理访问 GitHub。\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002FChandlerBang\u002Fawesome-self-supervised-gnn.git\ncd awesome-self-supervised-gnn\n```\n\n### 2. 选择目标项目\n浏览目录中的 `README.md` 文件，按年份（如 `Year 2023`, `Year 2024`）查找你感兴趣的论文。每个条目都提供了 **Paper** (论文链接) 和 **Code** (代码仓库链接)。\n\n### 3. 安装具体项目的依赖\n点击对应论文的 `[[code]]` 链接进入其独立的 GitHub 仓库，按照该项目具体的说明进行安装。通常步骤如下：\n\n```bash\n# 假设你选择了 \"GTrans\" 项目 (ICLR 2023)\ngit clone https:\u002F\u002Fgithub.com\u002FChandlerBang\u002FGTrans.git\ncd GTrans\n\n# 创建虚拟环境 (推荐)\npython -m venv venv\nsource venv\u002Fbin\u002Factivate  # Windows 用户使用: venv\\Scripts\\activate\n\n# 安装依赖 (优先使用国内镜像源加速)\npip install -r requirements.txt -i https:\u002F\u002Fpypi.tuna.tsinghua.edu.cn\u002Fsimple\n```\n\n*注意：如果目标项目未提供 `requirements.txt`，请根据论文描述手动安装 `torch`, `dgl` 或 `torch-geometric` 等核心库。*\n\n## 基本使用\n\n本仓库的核心用法是**作为索引查找代码**，然后运行具体项目的脚本。以下以列表中 2023 年的热门项目 **GTrans** 为例，展示典型的复现流程：\n\n### 示例：运行 GTrans (Test-Time Graph Transformation)\n\n1.  **获取数据**：\n    大多数 GNN 项目会自动下载标准数据集（如 Cora, Citeseer, Pubmed）。确保网络通畅，或手动下载数据集放置于项目指定的 `data\u002F` 目录下。\n\n2.  **执行训练\u002F评估**：\n    进入项目目录，运行提供的训练脚本。命令通常如下：\n\n    ```bash\n    python main.py --dataset cora --model gtrans\n    ```\n\n3.  **查看结果**：\n    程序运行结束后，终端将输出准确率（Accuracy）、NMI 或其他评估指标，结果通常也会保存至 `results\u002F` 文件夹。\n\n### 查找高引用论文\n仓库 README 中使用 :fire: 标记了高引用论文（引用数 > 80）。你可以运行仓库根目录下可能提供的辅助脚本（如原文提到的 `get_hot.py`，若存在）来筛选这些热门资源，或者直接阅读 README 中带有火焰图标的条目。\n\n```bash\n# 如果仓库包含此工具脚本\npython get_hot.py\n```\n\n通过上述步骤，你可以高效地利用 `awesome-self-supervised-gnn` 追踪最新技术，并在本地复现顶尖的自监督图学习算法。","某金融科技公司风控团队正试图利用图神经网络（GNN）从海量交易数据中识别隐蔽的洗钱团伙，但面临标注样本极度稀缺的困境。\n\n### 没有 awesome-self-supervised-gnn 时\n- **文献检索如大海捞针**：团队成员需手动在 arXiv、IEEE 等各大平台搜索“自监督”、“对比学习”等关键词，耗时数周仍难以覆盖最新进展，极易遗漏关键论文。\n- **复现成本高昂且盲目**：面对零散的代码库，无法快速判断哪些算法（如 GTrans 或 S-3-CL）真正适合当前稀疏标签场景，往往花费大量时间复现效果不佳的模型。\n- **技术选型缺乏依据**：难以区分哪些是仅停留在理论层面的研究，哪些是经过大规模验证（高引用）的成熟方案，导致项目初期技术路线频繁试错。\n- **领域知识更新滞后**：无法系统掌握从社区检测到异常识别等不同子任务的最新突破，错失利用结构化语义全局知识提升模型泛化能力的机会。\n\n### 使用 awesome-self-supervised-gnn 后\n- **一站式获取前沿成果**：直接按年份查阅整理好的论文清单，迅速锁定 2023-2024 年关于“单类同质性建模”或“测试时图变换”等针对性极强的最新研究。\n- **精准定位高价值代码**：通过仓库中标记的\"🔥\"高引用标识及配套的 `get_hot.py` 脚本，优先复现如 ParetoGNN 等已被社区验证有效的强泛化模型，大幅缩短研发周期。\n- **场景匹配高效明确**：依据分类目录快速找到专攻“图异常检测”或“属性缺失补全”的特定算法（如 Truncated Affinity Maximization），实现技术与业务痛点的无缝对接。\n- **紧跟学术演进脉络**：系统化追踪从基础对比学习到多任务自监督的技术演变，为团队制定长期技术路线图提供了坚实的文献支撑。\n\nawesome-self-supervised-gnn 将原本分散杂乱的学术资源转化为结构化的技术资产，帮助开发者在低资源场景下快速构建高性能图智能应用。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FChandlerBang_awesome-self-supervised-gnn_220cce48.png","ChandlerBang","Wei Jin","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002FChandlerBang_dda46d4b.png","Assistant Professor @ Emory University\r\n#MachineLearning #GraphNeuralNetworks\r\n","Emory University","Atlanta, Georgia",null,"weisshelter","http:\u002F\u002Fwww.cs.emory.edu\u002F~wjin30\u002F","https:\u002F\u002Fgithub.com\u002FChandlerBang",[86],{"name":87,"color":88,"percentage":89},"Python","#3572A5",100,1717,165,"2026-03-26T03:01:01",5,"","未说明",{"notes":97,"python":95,"dependencies":98},"该仓库是一个论文列表（Awesome List），而非具体的软件工具或代码库。它整理了关于图神经网络自监督学习的学术论文链接，部分条目提供了指向独立代码仓库的外部链接，但本仓库本身不包含可执行的源代码、模型文件或环境配置要求。因此，无法提取具体的运行环境需求。",[],[13],[101,102,103,104,105,106,107,108],"graph-neural-networks","pretraining","self-supervised-learning","deep-learning","machine-learning","graph-mining","pre-training","graph-self-supervised-learning","2026-03-27T02:49:30.150509","2026-04-06T10:24:52.791519",[],[]]