[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-naganandy--graph-based-deep-learning-literature":3,"tool-naganandy--graph-based-deep-learning-literature":61},[4,18,26,36,44,53],{"id":5,"name":6,"github_repo":7,"description_zh":8,"stars":9,"difficulty_score":10,"last_commit_at":11,"category_tags":12,"status":17},4358,"openclaw","openclaw\u002Fopenclaw","OpenClaw 是一款专为个人打造的本地化 AI 助手，旨在让你在自己的设备上拥有完全可控的智能伙伴。它打破了传统 AI 助手局限于特定网页或应用的束缚，能够直接接入你日常使用的各类通讯渠道，包括微信、WhatsApp、Telegram、Discord、iMessage 等数十种平台。无论你在哪个聊天软件中发送消息，OpenClaw 都能即时响应，甚至支持在 macOS、iOS 和 Android 设备上进行语音交互，并提供实时的画布渲染功能供你操控。\n\n这款工具主要解决了用户对数据隐私、响应速度以及“始终在线”体验的需求。通过将 AI 部署在本地，用户无需依赖云端服务即可享受快速、私密的智能辅助，真正实现了“你的数据，你做主”。其独特的技术亮点在于强大的网关架构，将控制平面与核心助手分离，确保跨平台通信的流畅性与扩展性。\n\nOpenClaw 非常适合希望构建个性化工作流的技术爱好者、开发者，以及注重隐私保护且不愿被单一生态绑定的普通用户。只要具备基础的终端操作能力（支持 macOS、Linux 及 Windows WSL2），即可通过简单的命令行引导完成部署。如果你渴望拥有一个懂你",349277,3,"2026-04-06T06:32:30",[13,14,15,16],"Agent","开发框架","图像","数据工具","ready",{"id":19,"name":20,"github_repo":21,"description_zh":22,"stars":23,"difficulty_score":10,"last_commit_at":24,"category_tags":25,"status":17},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,"2026-04-05T11:01:52",[14,15,13],{"id":27,"name":28,"github_repo":29,"description_zh":30,"stars":31,"difficulty_score":32,"last_commit_at":33,"category_tags":34,"status":17},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",160411,2,"2026-04-18T23:33:24",[14,13,35],"语言模型",{"id":37,"name":38,"github_repo":39,"description_zh":40,"stars":41,"difficulty_score":32,"last_commit_at":42,"category_tags":43,"status":17},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。",109154,"2026-04-18T11:18:24",[14,15,13],{"id":45,"name":46,"github_repo":47,"description_zh":48,"stars":49,"difficulty_score":32,"last_commit_at":50,"category_tags":51,"status":17},6121,"gemini-cli","google-gemini\u002Fgemini-cli","gemini-cli 是一款由谷歌推出的开源 AI 命令行工具，它将强大的 Gemini 大模型能力直接集成到用户的终端环境中。对于习惯在命令行工作的开发者而言，它提供了一条从输入提示词到获取模型响应的最短路径，无需切换窗口即可享受智能辅助。\n\n这款工具主要解决了开发过程中频繁上下文切换的痛点，让用户能在熟悉的终端界面内直接完成代码理解、生成、调试以及自动化运维任务。无论是查询大型代码库、根据草图生成应用，还是执行复杂的 Git 操作，gemini-cli 都能通过自然语言指令高效处理。\n\n它特别适合广大软件工程师、DevOps 人员及技术研究人员使用。其核心亮点包括支持高达 100 万 token 的超长上下文窗口，具备出色的逻辑推理能力；内置 Google 搜索、文件操作及 Shell 命令执行等实用工具；更独特的是，它支持 MCP（模型上下文协议），允许用户灵活扩展自定义集成，连接如图像生成等外部能力。此外，个人谷歌账号即可享受免费的额度支持，且项目基于 Apache 2.0 协议完全开源，是提升终端工作效率的理想助手。",100752,"2026-04-10T01:20:03",[52,13,15,14],"插件",{"id":54,"name":55,"github_repo":56,"description_zh":57,"stars":58,"difficulty_score":32,"last_commit_at":59,"category_tags":60,"status":17},4721,"markitdown","microsoft\u002Fmarkitdown","MarkItDown 是一款由微软 AutoGen 团队打造的轻量级 Python 工具，专为将各类文件高效转换为 Markdown 格式而设计。它支持 PDF、Word、Excel、PPT、图片（含 OCR）、音频（含语音转录）、HTML 乃至 YouTube 链接等多种格式的解析，能够精准提取文档中的标题、列表、表格和链接等关键结构信息。\n\n在人工智能应用日益普及的今天，大语言模型（LLM）虽擅长处理文本，却难以直接读取复杂的二进制办公文档。MarkItDown 恰好解决了这一痛点，它将非结构化或半结构化的文件转化为模型“原生理解”且 Token 效率极高的 Markdown 格式，成为连接本地文件与 AI 分析 pipeline 的理想桥梁。此外，它还提供了 MCP（模型上下文协议）服务器，可无缝集成到 Claude Desktop 等 LLM 应用中。\n\n这款工具特别适合开发者、数据科学家及 AI 研究人员使用，尤其是那些需要构建文档检索增强生成（RAG）系统、进行批量文本分析或希望让 AI 助手直接“阅读”本地文件的用户。虽然生成的内容也具备一定可读性，但其核心优势在于为机器",93400,"2026-04-06T19:52:38",[52,14],{"id":62,"github_repo":63,"name":64,"description_en":65,"description_zh":66,"ai_summary_zh":66,"readme_en":67,"readme_zh":68,"quickstart_zh":69,"use_case_zh":70,"hero_image_url":71,"owner_login":72,"owner_name":73,"owner_avatar_url":74,"owner_bio":73,"owner_company":73,"owner_location":73,"owner_email":75,"owner_twitter":73,"owner_website":73,"owner_url":76,"languages":77,"stars":82,"forks":83,"last_commit_at":84,"license":85,"difficulty_score":86,"env_os":87,"env_gpu":88,"env_ram":88,"env_deps":89,"category_tags":92,"github_topics":93,"view_count":32,"oss_zip_url":73,"oss_zip_packed_at":73,"status":17,"created_at":101,"updated_at":102,"faqs":103,"releases":138},9345,"naganandy\u002Fgraph-based-deep-learning-literature","graph-based-deep-learning-literature","links to conference publications in graph-based deep learning","graph-based-deep-learning-literature 是一个专为图深度学习领域打造的学术资源导航库。面对该领域论文爆发式增长、会议众多且分散的现状，它系统性地整理了来自 NeurIPS、ICML、LoG 等顶级会议的最新出版物链接，并涵盖了相关研讨会、综述文章、书籍以及实用的软件库。\n\n这一资源库有效解决了研究人员在海量文献中“大海捞针”的痛点。它将原本散落在各处的学术成果，按会议年份和具体主题进行了精细化的分类归档，让用户无需在不同网站间反复跳转，即可一站式获取从 2015 年至今的关键研究资料。无论是追踪前沿算法突破，还是回顾经典理论演变，都能在这里找到清晰的路径。\n\n该工具特别适合人工智能领域的研究人员、高校师生以及从事图神经网络开发的工程师使用。对于希望快速入门该领域的学生，或是需要全面掌握行业动态的资深专家，它都是一份不可或缺的案头指南。其独特的亮点在于不仅提供论文索引，还关联了代码实现与综述资源，形成了从理论到实践的完整闭环，极大地提升了科研调研与工程落地的效率。","# Graph-based Deep Learning Literature\n\nThe repository primarily contains [links to conference publications](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002FREADME.md#conferences) in graph-based deep learning.\n\nThe repository also contains links to:\n\n- [Related Workshops](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fworkshops\u002FREADME.md),\n- [Surveys \u002F Literature Reviews \u002F Books](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fsurveys\u002FREADME.md), \n- [Software \u002F Libraries](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fsoftware\u002FREADME.md).\n \n \u003Cbr> \u003C\u002Fbr>\n\nPublications within each conference and year below are organised into topic-specific categories.\n\n\n\n* ### [Learning On Graphs Conference](https:\u002F\u002Flogconference.org\u002F) - [2024](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2024\u002Fpublications_log24\u002FREADME.md) |  [2023](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2023\u002Fpublications_log23\u002FREADME.md) | [2022](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2022\u002Fpublications_log22\u002FREADME.md)\n\n- ## Machine Learning Conferences\n \n   * ### [NeurIPS](https:\u002F\u002Fnips.cc\u002F)  - [2025](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2025\u002Fpublications_neurips25\u002FREADME.md) | [2024](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2024\u002Fpublications_neurips24\u002FREADME.md) | [2023](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2023\u002Fpublications_neurips23\u002FREADME.md) | [2022](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2022\u002Fpublications_neurips22\u002FREADME.md) | [2021](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2021\u002Fpublications_neurips21\u002FREADME.md) | [2020](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2020\u002Fpublications_neurips20\u002FREADME.md) | [2019](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2019\u002Fpublications_neurips19\u002FREADME.md) | [2018](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2018\u002Fpublications_neurips18\u002FREADME.md) | [2017](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2017_and_Earlier\u002FREADME.MD#neurips-2017) | [2016](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2017_and_Earlier\u002FREADME.MD#neurips-2016) | [2015](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2017_and_Earlier\u002FREADME.MD#neurips-2015)\n\n   * ### [ICML](https:\u002F\u002Ficml.cc\u002F) - [2025](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2025\u002Fpublications_icml25\u002FREADME.md) | [2024](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2024\u002Fpublications_icml24\u002FREADME.md) | [2023](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2023\u002Fpublications_icml23\u002FREADME.md) | [2022](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2022\u002Fpublications_icml22\u002FREADME.md) | [2021](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2021\u002Fpublications_icml21\u002FREADME.md) |  [2020](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2020\u002Fpublications_icml20\u002FREADME.md) | [2019](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2019\u002Fpublications_icml19\u002FREADME.md) | [2018](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2018\u002FREADME.MD#icml-2018-jul) | [2017](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2017_and_Earlier\u002FREADME.MD#icml-2017) | [2016](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2017_and_Earlier\u002FREADME.MD#icml-2016)\n \n   * ### [ICLR](https:\u002F\u002Ficlr.cc\u002F) - [2026](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2026\u002Fpublications_iclr26\u002FREADME.md) | [2025](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2025\u002Fpublications_iclr25\u002FREADME.md) | [2024](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2024\u002Fpublications_iclr24\u002FREADME.md) | [2023](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2023\u002Fpublications_iclr23\u002FREADME.md) | [2022](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2022\u002Fpublications_iclr22\u002FREADME.md) | [2021](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2021\u002Fpublications_iclr21\u002FREADME.md) | [2020](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2020\u002Fpublications_iclr20\u002FREADME.md) | [2019](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2019\u002Fpublications_iclr19\u002FREADME.md) | [2018](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2018\u002FREADME.MD#iclr-2018-may) | [2017](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2017_and_Earlier\u002FREADME.MD#iclr-2017) | [2016](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2017_and_Earlier\u002FREADME.MD#iclr-2016) | [2014](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2017_and_Earlier\u002FREADME.MD#iclr-2014)\n\n\u003Cbr> \u003C\u002Fbr>\n   \n- ## Data Mining Conferences\n   * ### [KDD](https:\u002F\u002Fkdd2026.kdd.org\u002F) - [2025](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Ftree\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2025\u002Fpublications_kdd25\u002FREADME.md) | [2024](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Ftree\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2024\u002Fpublications_kdd24\u002FREADME.md) | [2023](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Ftree\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2023\u002Fpublications_kdd23\u002FREADME.md) | [2022](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Ftree\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2022\u002Fpublications_kdd22\u002FREADME.md) | [2021](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Ftree\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2021\u002Fpublications_kdd21\u002FREADME.md) | [2020](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Ftree\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2020\u002Fpublications_kdd20\u002FREADME.md) | [2019](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2019\u002Fpublications_kdd19\u002FREADME.md) | [2018](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2018\u002FREADME.MD#kdd-2018-aug)\n   * ### [ICDM](https:\u002F\u002Ficdm2025.org\u002F) - [2024](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2024\u002Fpublications_icdm24\u002FREADME.md) | [2023](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2023\u002Fpublications_icdm23\u002FREADME.md) | [2022](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2022\u002Fpublications_icdm22\u002FREADME.md) | [2021](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2021\u002Fpublications_icdm21\u002FREADME.md) | [2020](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2020\u002Fpublications_icdm20\u002FREADME.md) | [2019](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2019\u002FREADME.MD#icdm-2019-nov) | [2018](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2018\u002FREADME.MD#icdm-2018-nov)\n   * ### [WSDM](https:\u002F\u002Fwww.wsdm-conference.org\u002F2026\u002F) - [2025](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2025\u002Fpublications_wsdm25\u002FREADME.md) | [2024](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2024\u002Fpublications_wsdm24\u002FREADME.md) | [2023](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2023\u002Fpublications_wsdm23\u002FREADME.md) | [2022](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2022\u002Fpublications_wsdm22\u002FREADME.md) | [2021](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2021\u002Fpublications_wsdm21\u002FREADME.md) | [2020](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2020\u002FREADME.MD#wsdm-2020-feb) | [2019](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2019\u002FREADME.MD#wsdm-2019-jan) \n\n\u003Cbr> \u003C\u002Fbr>\n\n- ## Artificial Intelligence Conferences\n   * ### [TheWebConf](https:\u002F\u002Fwww2026.thewebconf.org\u002F) - [2025](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2025\u002Fpublications_webconf25\u002FREADME.md)  | [2024](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2024\u002Fpublications_webconf24\u002FREADME.md)  | [2023](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2023\u002Fpublications_webconf23\u002FREADME.md)  |  [2022](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2022\u002Fpublications_webconf22\u002FREADME.md)  | [2021](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2021\u002Fpublications_webconf21\u002FREADME.md) | [2020](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2020\u002Fpublications_www20\u002FREADME.md) | [2019](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2019\u002FREADME.MD#www-2019-may) | [2018](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2018\u002FREADME.MD#www-2018-april)\n   * ### [AAAI](https:\u002F\u002Faaai.org\u002Fconference\u002Faaai\u002Faaai-26\u002F) - [2026](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2026\u002Fpublications_aaai26\u002FREADME.md) | [2025](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2025\u002Fpublications_aaai25\u002FREADME.md) |  [2024](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2024\u002Fpublications_aaai24\u002FREADME.md) |  [2023](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2023\u002Fpublications_aaai23\u002FREADME.md) | [2022](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2022\u002Fpublications_aaai22\u002FREADME.md) | [2021](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2021\u002Fpublications_aaai21\u002FREADME.md) | [2020](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2020\u002Fpublications_aaai20\u002FREADME.md) | [2019](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2019\u002Fpublications_aaai19\u002FREADME.md) | [2018](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2018\u002FREADME.MD#aaai-2018-feb) | [2017](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2017_and_Earlier\u002FREADME.MD#aaai-2017)\n   * ### [IJCAI](https:\u002F\u002F2025.ijcai.org\u002F) - [2025](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2025\u002Fpublications_ijcai25\u002FREADME.md) | [2024](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2024\u002Fpublications_ijcai24\u002FREADME.md) | [2023](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2023\u002Fpublications_ijcai23\u002FREADME.md) | [2022](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2022\u002Fpublications_ijcai22\u002FREADME.md) | [2021](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2021\u002Fpublications_ijcai21\u002FREADME.md) |  [2020](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2020\u002Fpublications_ijcai20\u002FREADME.md) | [2019](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2019\u002Fpublications_ijcai19\u002FREADME.md) | [2018](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2018\u002FREADME.MD#ijcai-2018-jul) | [2017](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2017_and_Earlier\u002FREADME.MD#ijcai-2017)\n\n\u003Cbr> \u003C\u002Fbr>\n\n- ## Computer Vision Conferences\n   * ### [CVPR](https:\u002F\u002Fcvpr.thecvf.com\u002F) - [2025](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2025\u002Fpublications_cvpr25\u002FREADME.md) | [2024](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2024\u002Fpublications_cvpr24\u002FREADME.md) | [2023](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2023\u002Fpublications_cvpr23\u002FREADME.md) | [2022](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2022\u002Fpublications_cvpr22\u002FREADME.md) | [2021](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2021\u002Fpublications_cvpr21\u002FREADME.md) | [2020](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2020\u002Fpublications_cvpr20\u002FREADME.md) | [2019](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2019\u002Fpublications_cvpr19\u002FREADME.md) | [2018](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2018\u002FREADME.MD#cvpr-2018-jun) | [2017](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2017_and_Earlier\u002FREADME.MD#cvpr-2017)\n   * ### [ICCV](https:\u002F\u002Ficcv.thecvf.com\u002F) - [2023](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2023\u002Fpublications_iccv23\u002FREADME.md) | [2021](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2021\u002Fpublications_iccv21\u002FREADME.md) | [2019](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2019\u002Fpublications_iccv19\u002FREADME.md) | [2017](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2017_and_Earlier\u002FREADME.MD#iccv-2017)\n   * ### [ECCV](https:\u002F\u002Feccv2024.ecva.net\u002F) - [2024](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2024\u002Fpublications_eccv24\u002FREADME.md) | [2022](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2022\u002Fpublications_eccv22\u002FREADME.md) | [2020](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2020\u002Fpublications_eccv20\u002FREADME.md) | [2018](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2018\u002FREADME.MD#eccv-2018-sep)\n\n\u003Cbr> \u003C\u002Fbr>\n\n- ## Computational Linguistics Conferences\n   * ### [ACL](https:\u002F\u002F2025.aclweb.org\u002F) - [2025](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2025\u002Fpublications_acl25\u002FREADME.md) | [2024](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2024\u002Fpublications_acl24\u002FREADME.md) | [2023](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2023\u002Fpublications_acl23\u002FREADME.md) | [2022](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2022\u002Fpublications_acl22\u002FREADME.md) | [2021](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2021\u002Fpublications_acl21\u002FREADME.md) | [2020](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2020\u002Fpublications_acl20\u002FREADME.md) | [2019](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2019\u002Fpublications_acl19\u002FREADME.md) | [2018](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2018\u002FREADME.MD#acl-2018-jul)\n    * ### [EMNLP](https:\u002F\u002F2025.emnlp.org\u002F) - [2024](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2024\u002Fpublications_emnlp24\u002FREADME.md) | [2023](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2023\u002Fpublications_emnlp23\u002FREADME.md) | [2022](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2022\u002Fpublications_emnlp22\u002FREADME.md) | [2021](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2021\u002Fpublications_emnlp21\u002FREADME.md) | [2020](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2020\u002Fpublications_emnlp20\u002FREADME.md) | [2019](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2019\u002Fpublications_emnlp19\u002FREADME.md) | [2018](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2018\u002FREADME.MD#emnlp-2018-nov) | [2017](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2017_and_Earlier\u002FREADME.MD#emnlp-2017)\n  * ### [NAACL](https:\u002F\u002F2025.naacl.org\u002F) - [2025](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2025\u002Fpublications_naacl25\u002FREADME.md) | [2024](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2024\u002Fpublications_naacl24\u002FREADME.md) | [2022](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2022\u002Fpublications_naacl22\u002FREADME.md) | [2021](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2021\u002Fpublications_naacl21\u002FREADME.md) | [2019](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2019\u002FREADME.MD#naacl-2019-jun) | [2018](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2018\u002FREADME.MD#naacl-2018-jun)\n\n\u003Cbr> \u003C\u002Fbr>\n\u003Cbr> \u003C\u002Fbr>\n\u003Cbr> \u003C\u002Fbr>\n\n# Top 10 Most Cited Publications (on Graph Neural Networks)\n- [Semi-Supervised Classification with Graph Convolutional Networks](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2017_and_Earlier\u002Fgcn_iclr17\u002FREADME.md)\n- [Graph Attention Networks](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2018\u002Fpublications_conf18\u002Fgan_iclr18\u002FREADME.md)\n- [Inductive Representation Learning on Large Graphs](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2017_and_Earlier\u002Fgraphsage_neurips17\u002FREADME.md)\n- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2017_and_Earlier\u002Fchebnet_neurips16\u002FREADME.md)\n- [The Graph Neural Network Model](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2017_and_Earlier\u002Fgnn_tnn09\u002FREADME.md)\n- [A Comprehensive Survey on Graph Neural Networks](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fsurveys\u002Fgnnsurvey_tnnls21\u002FREADME.md)\n- [Neural Message Passing for Quantum Chemistry](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2017_and_Earlier\u002Fmpnn_icml17\u002FREADME.md)\n- [Spectral Networks and Locally Connected Networks on Graphs](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2017_and_Earlier\u002Fgraphcnn_iclr14\u002FREADME.md)\n- [How Powerful are Graph Neural Networks?](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2019\u002Fpublications_iclr19\u002Fgin_iclr19\u002FREADME.md)\n- [Convolutional Networks on Graphs for Learning Molecular Fingerprints](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2017_and_Earlier\u002Fgraphcnn_neurips15\u002FREADME.md)\n","# 基于图的深度学习文献\n\n该仓库主要包含基于图的深度学习领域会议论文的[链接](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002FREADME.md#conferences)。\n\n此外，仓库还包含以下内容的链接：\n\n- [相关研讨会](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fworkshops\u002FREADME.md)，\n- [综述\u002F文献评论\u002F书籍](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fsurveys\u002FREADME.md)， \n- [软件\u002F库](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fsoftware\u002FREADME.md)。\n \n \u003Cbr> \u003C\u002Fbr>\n\n下方各会议及年份内的论文按主题分类组织。\n\n\n\n* ### [图上学习会议](https:\u002F\u002Flogconference.org\u002F) - [2024年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2024\u002Fpublications_log24\u002FREADME.md) |  [2023年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2023\u002Fpublications_log23\u002FREADME.md) | [2022年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2022\u002Fpublications_log22\u002FREADME.md)\n\n- ## 机器学习会议\n \n   * ### [NeurIPS](https:\u002F\u002Fnips.cc\u002F)  - [2025年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2025\u002Fpublications_neurips25\u002FREADME.md) | [2024年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2024\u002Fpublications_neurips24\u002FREADME.md) | [2023年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2023\u002Fpublications_neurips23\u002FREADME.md) | [2022年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2022\u002Fpublications_neurips22\u002FREADME.md) | [2021年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2021\u002Fpublications_neurips21\u002FREADME.md) | [2020年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2020\u002Fpublications_neurips20\u002FREADME.md) | [2019年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2019\u002Fpublications_neurips19\u002FREADME.md) | [2018年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2018\u002Fpublications_neurips18\u002FREADME.md) | [2017年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2017_and_Earlier\u002FREADME.MD#neurips-2017) | [2016年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2017_and_Earlier\u002FREADME.MD#neurips-2016) | [2015年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2017_and_Earlier\u002FREADME.MD#neurips-2015)\n\n   * ### [ICML](https:\u002F\u002Ficml.cc\u002F) - [2025年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2025\u002Fpublications_icml25\u002FREADME.md) | [2024年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2024\u002Fpublications_icml24\u002FREADME.md) | [2023年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2023\u002Fpublications_icml23\u002FREADME.md) | [2022年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2022\u002Fpublications_icml22\u002FREADME.md) | [2021年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2021\u002Fpublications_icml21\u002FREADME.md) |  [2020年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2020\u002Fpublications_icml20\u002FREADME.md) | [2019年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2019\u002Fpublications_icml19\u002FREADME.md) | [2018年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2018\u002FREADME.MD#icml-2018-jul) | [2017年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2017_and_Earlier\u002FREADME.MD#icml-2017) | [2016年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2017_and_Earlier\u002FREADME.MD#icml-2016)\n \n   * ### [ICLR](https:\u002F\u002Ficlr.cc\u002F) - [2026年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2026\u002Fpublications_iclr26\u002FREADME.md) | [2025年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2025\u002Fpublications_iclr25\u002FREADME.md) | [2024年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2024\u002Fpublications_iclr24\u002FREADME.md) | [2023年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2023\u002Fpublications_iclr23\u002FREADME.md) | [2022年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2022\u002Fpublications_iclr22\u002FREADME.md) | [2021年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2021\u002Fpublications_iclr21\u002FREADME.md) | [2020年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2020\u002Fpublications_iclr20\u002FREADME.md) | [2019年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2019\u002Fpublications_iclr19\u002FREADME.md) | [2018年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2018\u002FREADME.MD#iclr-2018-may) | [2017年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2017_and_Earlier\u002FREADME.MD#iclr-2017) | [2016年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2017_and_Earlier\u002FREADME.MD#iclr-2016) | [2014年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2017_and_Earlier\u002FREADME.MD#iclr-2014)\n\n\u003Cbr> \u003C\u002Fbr>\n   \n- ## 数据挖掘会议\n   * ### [KDD](https:\u002F\u002Fkdd2026.kdd.org\u002F) - [2025年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Ftree\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2025\u002Fpublications_kdd25\u002FREADME.md) | [2024年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Ftree\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2024\u002Fpublications_kdd24\u002FREADME.md) | [2023年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Ftree\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2023\u002Fpublications_kdd23\u002FREADME.md) | [2022年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Ftree\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2022\u002Fpublications_kdd22\u002FREADME.md) | [2021年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Ftree\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2021\u002Fpublications_kdd21\u002FREADME.md) | [2020年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Ftree\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2020\u002Fpublications_kdd20\u002FREADME.md) | [2019年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2019\u002Fpublications_kdd19\u002FREADME.md) | [2018年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2018\u002FREADME.MD#kdd-2018-aug)\n   * ### [ICDM](https:\u002F\u002Ficdm2025.org\u002F) - [2024年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2024\u002Fpublications_icdm24\u002FREADME.md) | [2023年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2023\u002Fpublications_icdm23\u002FREADME.md) | [2022年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2022\u002Fpublications_icdm22\u002FREADME.md) | [2021年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2021\u002Fpublications_icdm21\u002FREADME.md) | [2020年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2020\u002Fpublications_icdm20\u002FREADME.md) | [2019年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2019\u002FREADME.MD#icdm-2019-nov) | [2018年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2018\u002FREADME.MD#icdm-2018-nov)\n   * ### [WSDM](https:\u002F\u002Fwww.wsdm-conference.org\u002F2026\u002F) - [2025年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2025\u002Fpublications_wsdm25\u002FREADME.md) | [2024年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2024\u002Fpublications_wsdm24\u002FREADME.md) | [2023年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2023\u002Fpublications_wsdm23\u002FREADME.md) | [2022年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2022\u002Fpublications_wsdm22\u002FREADME.md) | [2021年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2021\u002Fpublications_wsdm21\u002FREADME.md) | [2020年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2020\u002FREADME.MD#wsdm-2020-feb) | [2019年](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2019\u002FREADME.MD#wsdm-2019-jan) \n\n\u003Cbr> \u003C\u002Fbr>\n\n- ## 人工智能会议\n   * ### [TheWebConf](https:\u002F\u002Fwww2026.thewebconf.org\u002F) - [2025](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2025\u002Fpublications_webconf25\u002FREADME.md)  | [2024](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2024\u002Fpublications_webconf24\u002FREADME.md)  | [2023](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2023\u002Fpublications_webconf23\u002FREADME.md)  |  [2022](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2022\u002Fpublications_webconf22\u002FREADME.md)  | [2021](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2021\u002Fpublications_webconf21\u002FREADME.md) | [2020](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2020\u002Fpublications_www20\u002FREADME.md) | [2019](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2019\u002FREADME.MD#www-2019-may) | [2018](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2018\u002FREADME.MD#www-2018-april)\n   * ### [AAAI](https:\u002F\u002Faaai.org\u002Fconference\u002Faaai\u002Faaai-26\u002F) - [2026](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2026\u002Fpublications_aaai26\u002FREADME.md) | [2025](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2025\u002Fpublications_aaai25\u002FREADME.md) |  [2024](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2024\u002Fpublications_aaai24\u002FREADME.md) |  [2023](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2023\u002Fpublications_aaai23\u002FREADME.md) | [2022](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2022\u002Fpublications_aaai22\u002FREADME.md) | [2021](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2021\u002Fpublications_aaai21\u002FREADME.md) | [2020](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2020\u002Fpublications_aaai20\u002FREADME.md) | [2019](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2019\u002Fpublications_aaai19\u002FREADME.md) | [2018](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2018\u002FREADME.MD#aaai-2018-feb) | [2017](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2017_and_Earlier\u002FREADME.MD#aaai-2017)\n   * ### [IJCAI](https:\u002F\u002F2025.ijcai.org\u002F) - [2025](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2025\u002Fpublications_ijcai25\u002FREADME.md) | [2024](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2024\u002Fpublications_ijcai24\u002FREADME.md) | [2023](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2023\u002Fpublications_ijcai23\u002FREADME.md) | [2022](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2022\u002Fpublications_ijcai22\u002FREADME.md) | [2021](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2021\u002Fpublications_ijcai21\u002FREADME.md) |  [2020](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2020\u002Fpublications_ijcai20\u002FREADME.md) | [2019](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2019\u002Fpublications_ijcai19\u002FREADME.md) | [2018](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2018\u002FREADME.MD#ijcai-2018-jul) | [2017](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2017_and_Earlier\u002FREADME.MD#ijcai-2017)\n\n\u003Cbr> \u003C\u002Fbr>\n\n- ## 计算机视觉会议\n   * ### [CVPR](https:\u002F\u002Fcvpr.thecvf.com\u002F) - [2025](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2025\u002Fpublications_cvpr25\u002FREADME.md) | [2024](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2024\u002Fpublications_cvpr24\u002FREADME.md) | [2023](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2023\u002Fpublications_cvpr23\u002FREADME.md) | [2022](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2022\u002Fpublications_cvpr22\u002FREADME.md) | [2021](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2021\u002Fpublications_cvpr21\u002FREADME.md) | [2020](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2020\u002Fpublications_cvpr20\u002FREADME.md) | [2019](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2019\u002Fpublications_cvpr19\u002FREADME.md) | [2018](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2018\u002FREADME.MD#cvpr-2018-jun) | [2017](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2017_and_Earlier\u002FREADME.MD#cvpr-2017)\n   * ### [ICCV](https:\u002F\u002Ficcv.thecvf.com\u002F) - [2023](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2023\u002Fpublications_iccv23\u002FREADME.md) | [2021](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2021\u002Fpublications_iccv21\u002FREADME.md) | [2019](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2019\u002Fpublications_iccv19\u002FREADME.md) | [2017](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2017_and_Earlier\u002FREADME.MD#iccv-2017)\n   * ### [ECCV](https:\u002F\u002Feccv2024.ecva.net\u002F) - [2024](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2024\u002Fpublications_eccv24\u002FREADME.md) | [2022](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2022\u002Fpublications_eccv22\u002FREADME.md) | [2020](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2020\u002Fpublications_eccv20\u002FREADME.md) | [2018](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2018\u002FREADME.MD#eccv-2018-sep)\n\n\u003Cbr> \u003C\u002Fbr>\n\n- ## 计算语言学会议\n   * ### [ACL](https:\u002F\u002F2025.aclweb.org\u002F) - [2025](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2025\u002Fpublications_acl25\u002FREADME.md) | [2024](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2024\u002Fpublications_acl24\u002FREADME.md) | [2023](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2023\u002Fpublications_acl23\u002FREADME.md) | [2022](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2022\u002Fpublications_acl22\u002FREADME.md) | [2021](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2021\u002Fpublications_acl21\u002FREADME.md) | [2020](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2020\u002Fpublications_acl20\u002FREADME.md) | [2019](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2019\u002Fpublications_acl19\u002FREADME.md) | [2018](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2018\u002FREADME.MD#acl-2018-jul)\n    * ### [EMNLP](https:\u002F\u002F2025.emnlp.org\u002F) - [2024](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2024\u002Fpublications_emnlp24\u002FREADME.md) | [2023](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2023\u002Fpublications_emnlp23\u002FREADME.md) | [2022](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2022\u002Fpublications_emnlp22\u002FREADME.md) | [2021](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2021\u002Fpublications_emnlp21\u002FREADME.md) | [2020](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2020\u002Fpublications_emnlp20\u002FREADME.md) | [2019](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2019\u002Fpublications_emnlp19\u002FREADME.md) | [2018](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2018\u002FREADME.MD#emnlp-2018-nov) | [2017](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2017_and_Earlier\u002FREADME.MD#emnlp-2017)\n  * ### [NAACL](https:\u002F\u002F2025.naacl.org\u002F) - [2025](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2025\u002Fpublications_naacl25\u002FREADME.md) | [2024](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2024\u002Fpublications_naacl24\u002FREADME.md) | [2022](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2022\u002Fpublications_naacl22\u002FREADME.md) | [2021](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2021\u002Fpublications_naacl21\u002FREADME.md) | [2019](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2019\u002FREADME.MD#naacl-2019-jun) | [2018](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2018\u002FREADME.MD#naacl-2018-jun)\n\n\u003Cbr> \u003C\u002Fbr>\n\u003Cbr> \u003C\u002Firl>\n\u003Cbr> \u003C\u002Firl>\n\n# 图神经网络领域被引用最多的10篇论文\n- [基于图卷积网络的半监督分类](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2017_and_Earlier\u002Fgcn_iclr17\u002FREADME.md)\n- [图注意力网络](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2018\u002Fpublications_conf18\u002Fgan_iclr18\u002FREADME.md)\n- [大规模图上的归纳式表示学习](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2017_and_Earlier\u002Fgraphsage_neurips17\u002FREADME.md)\n- [基于快速局部化谱滤波的图卷积神经网络](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2017_and_Earlier\u002Fchebnet_neurips16\u002FREADME.md)\n- [图神经网络模型](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2017_and_Earlier\u002Fgnn_tnn09\u002FREADME.md)\n- [图神经网络的全面综述](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fsurveys\u002Fgnnsurvey_tnnls21\u002FREADME.md)\n- [用于量子化学的神经消息传递](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2017_and_Earlier\u002Fmpnn_icml17\u002FREADME.md)\n- [图上的谱网络和局部连接网络](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2017_and_Earlier\u002Fgraphcnn_iclr14\u002FREADME.md)\n- [图神经网络有多强大？](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2019\u002Fpublications_iclr19\u002Fgin_iclr19\u002FREADME.md)\n- [用于学习分子指纹的图卷积网络](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fyears\u002F2017_and_Earlier\u002Fgraphcnn_neurips15\u002FREADME.md)","# Graph-based Deep Learning Literature 快速上手指南\n\n本仓库并非软件库或框架，而是一个**图深度学习（Graph-based Deep Learning）领域的学术文献索引库**。它整理了顶级会议中关于图神经网络及相关技术的论文链接、综述、研讨会信息及软件库推荐。因此，本指南侧重于如何高效浏览和检索该仓库内容，无需安装任何依赖。\n\n## 环境准备\n\n由于本仓库仅为 Markdown 文档集合，**无需配置任何系统环境或安装前置依赖**。\n\n- **系统要求**：任意操作系统（Windows, macOS, Linux）。\n- **所需工具**：\n  - 现代 Web 浏览器（推荐 Chrome, Edge, Firefox）用于在线浏览。\n  - 或 Git 客户端（可选），用于克隆仓库到本地离线阅读。\n\n## 获取内容\n\n你可以选择直接在线浏览，或克隆到本地以便搜索和整理。\n\n### 方式一：在线浏览（推荐）\n直接访问 GitHub 仓库页面即可开始阅读，无需任何命令：\n> https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\n\n### 方式二：本地克隆（适合深度检索）\n如果你希望在本地使用文本编辑器或 IDE 进行全文搜索，可执行以下命令：\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature.git\ncd graph-based-deep-learning-literature\n```\n\n*注：国内用户若遇到克隆速度慢的问题，可使用国内代码托管平台（如 Gitee）的镜像源，或通过设置 Git 代理加速。*\n\n## 基本使用\n\n本仓库的内容按**会议类型**、**年份**及**主题类别**进行层级化组织。以下是最高效的使用路径：\n\n### 1. 查找特定会议的论文\n在根目录的 `README.md` 中，找到你关注的顶级会议板块。仓库主要覆盖以下三类会议：\n\n- **机器学习顶会**：NeurIPS, ICML, ICLR\n- **数据挖掘顶会**：KDD, ICDM, WSDM\n- **人工智能与 Web 顶会**：TheWebConf (WWW), AAAI, IJCAI 等\n- **图学习专项会议**：Learning On Graphs Conference (LoG)\n\n点击对应的年份链接（例如 `2024` 或 `2023`），即可进入该年度的论文列表。\n\n### 2. 浏览特定主题的论文\n进入具体年份的页面后，论文已按技术主题分类（如 *Graph Neural Networks*, *Link Prediction*, *Graph Generation* 等）。直接点击主题分类标题，即可查看该细分领域下的论文标题及链接。\n\n### 3. 获取综述与软件库\n如果你需要入门综述或现成的代码库，请直接跳转至根目录中的特别章节：\n\n- **综述\u002F书籍**：点击 [Surveys \u002F Literature Reviews \u002F Books](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fsurveys\u002FREADME.md) 获取领域全景概览。\n- **软件\u002F库**：点击 [Software \u002F Libraries](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fsoftware\u002FREADME.md) 查找相关的开源实现工具。\n- **研讨会**：点击 [Related Workshops](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fblob\u002Fmaster\u002Fconference-publications\u002Ffolders\u002Fworkshops\u002FREADME.md) 了解前沿研讨内容。\n\n### 4. 检索特定论文\n若在本地克隆了仓库，推荐使用 VS Code 或 IntelliJ IDEA 打开文件夹，使用全局搜索功能（`Ctrl+Shift+F` 或 `Cmd+Shift+F`）输入关键词（如 \"Transformer\" 或 \"Contrastive\"），可快速定位到相关论文所在的会议和年份文件。","某高校实验室的博士生正在撰写关于“动态图神经网络在金融欺诈检测中应用”的综述论文，急需梳理近五年的核心会议成果。\n\n### 没有 graph-based-deep-learning-literature 时\n- **检索效率低下**：需要在 NeurIPS、ICML 等多个顶级会议的海量论文中手动筛选图深度学习相关文献，耗时数周且极易遗漏关键文章。\n- **分类整理困难**：找到的论文分散在不同年份和会议官网，缺乏统一的主题分类（如节点分类、链接预测），难以快速构建知识体系。\n- **资源获取不全**：容易忽略相关的研讨会（Workshops）论文或重要的开源代码库实现，导致文献调研不够全面，影响论文深度。\n- **追踪前沿滞后**：难以系统性地对比 2020 年至 2024 年间技术演进的脉络，往往只能依赖零散的引用关系来推测发展趋势。\n\n### 使用 graph-based-deep-learning-literature 后\n- **一站式精准获取**：直接访问按年份和会议（如 NeurIPS 2024、ICML 2023）整理的列表，几分钟内即可锁定所有图深度学习领域的顶会论文链接。\n- **结构化知识梳理**：利用仓库内预设的主题分类（Topic-specific categories），迅速将文献归入“动态图学习”或“欺诈检测”等具体方向，大幅缩短文献综述架构时间。\n- **生态资源全覆盖**：通过内置的“软件\u002F库”和“研讨会”板块，一次性补全了算法实现代码和边缘创新研究，确保调研无死角。\n- **演进脉络清晰**：借助从 2016 年至今的连续归档，直观对比不同年份的技术突破点，轻松绘制出该领域清晰的技术发展路线图。\n\ngraph-based-deep-learning-literature 将原本需要数周的非结构化信息搜集工作，转化为小时级的结构化知识获取，极大提升了科研人员的文献调研效率与质量。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fnaganandy_graph-based-deep-learning-literature_fb8bc18e.png","naganandy",null,"https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002Fnaganandy_b89f1dd9.png","y.naganand@gmail.com","https:\u002F\u002Fgithub.com\u002Fnaganandy",[78],{"name":79,"color":80,"percentage":81},"Jupyter Notebook","#DA5B0B",100,5049,785,"2026-04-18T09:07:35","MIT",1,"","未说明",{"notes":90,"python":88,"dependencies":91},"该仓库并非可执行的软件工具或代码库，而是一个图深度学习领域的文献索引列表。它主要包含指向会议论文、研讨会、综述文章以及相关软件库的链接。因此，该仓库本身没有操作系统、GPU、内存、Python 版本或依赖库的运行环境需求。用户只需具备访问 GitHub 和网络链接的能力即可浏览内容。",[],[14,16],[94,95,96,97,98,99,100],"graph-neural-networks","graph-convolutional-networks","graph","deep-learning","neural-networks","graph-representation-learning","conference-publications","2026-03-27T02:49:30.150509","2026-04-19T09:15:02.347011",[104,109,113,118,123,128,133],{"id":105,"question_zh":106,"answer_zh":107,"source_url":108},41924,"项目是否收录 ASONAM 或 WSDM 等会议的论文？","是的，项目不仅关注最高 tier 的会议，也收录其他会议上质量较高的论文。例如，来自 KDD、AAAI、NIPS 研讨会的论文，以及 ASONAM 或 WSDM 等会议上的优秀论文（如 KDD 18 的 GAM）都会被考虑添加。","https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fissues\u002F7",{"id":110,"question_zh":111,"answer_zh":112,"source_url":108},41918,"是否包含图表示学习研讨会（Workshop）的论文？","是的，项目可以为每个图表示学习研讨会提供一个链接。这些研讨会中的所有论文都是相关的，而且其中一些顶级论文（例如 NIPS 研讨会中的 DGI、LanczosNet）最终也会发表在顶级会议（如 ICLR 2019）上。",{"id":114,"question_zh":115,"answer_zh":116,"source_url":117},41919,"如何将关于对抗攻击鲁棒性或集合学习的论文归类？","维护者会根据论文主题将其归入相应类别。例如，关于“知识图谱多跳逻辑推理”的论文会被添加到“集合学习（Learning with Sets）”或“知识图谱”类别；关于通过优化互信息变分界来学习节点\u002F图表示并展示对抗攻击鲁棒性的论文，会被归类到“对抗攻击\u002F鲁棒性（adversarial attack\u002Frobustness）”类别。","https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fissues\u002F26",{"id":119,"question_zh":120,"answer_zh":121,"source_url":122},41920,"项目是否会收录 SIGIR 会议的论文？","是的，维护者计划收录 SIGIR 会议论文。具体操作是在接受论文名单公布后，添加该年度（如 2020 年）的高质量基于 GNN 的推荐和信息检索论文。","https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fissues\u002F20",{"id":124,"question_zh":125,"answer_zh":126,"source_url":127},41921,"如果发现某次会议（如 ICLR 或 AAAI）的论文收录不全怎么办？","用户可以在 Issue 中提醒维护者缺失的论文。维护者在收到反馈后，会核实并将缺失的论文补充到仓库中。","https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fissues\u002F36",{"id":129,"question_zh":130,"answer_zh":131,"source_url":132},41922,"如何提交新的相关论文或代码资源？","用户可以通过创建 Issue 或在现有讨论线程中评论来提交新资源。提交时请提供论文标题、会议来源、Arxiv 链接以及代码仓库链接（如有）。维护者确认后会将其添加到项目中。例如，用户可以提交双曲图神经网络相关的综述或特定应用论文。","https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fissues\u002F32",{"id":134,"question_zh":135,"answer_zh":136,"source_url":137},41923,"早期的无监督 GNN 论文（如 2017 年 NeurIPS）会被收录吗？","会的。该项目致力于全面收录，包括早期的开创性工作。例如，2017 年 NeurIPS 发表的关于嵌入传播（Embedding Propagation）的无监督 GNN 论文已被收录，即使它与同年发表的 GraphSage 类似。","https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature\u002Fissues\u002F22",[]]