[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-jdlc105--Must-read-papers-and-continuous-tracking-on-Graph-Neural-Network-GNN-progress":3,"tool-jdlc105--Must-read-papers-and-continuous-tracking-on-Graph-Neural-Network-GNN-progress":65},[4,23,32,40,49,57],{"id":5,"name":6,"github_repo":7,"description_zh":8,"stars":9,"difficulty_score":10,"last_commit_at":11,"category_tags":12,"status":22},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。",85092,2,"2026-04-10T11:13:16",[13,14,15,16,17,18,19,20,21],"图像","数据工具","视频","插件","Agent","其他","语言模型","开发框架","音频","ready",{"id":24,"name":25,"github_repo":26,"description_zh":27,"stars":28,"difficulty_score":29,"last_commit_at":30,"category_tags":31,"status":22},5784,"funNLP","fighting41love\u002FfunNLP","funNLP 是一个专为中文自然语言处理（NLP）打造的超级资源库，被誉为\"NLP 民工的乐园”。它并非单一的软件工具，而是一个汇集了海量开源项目、数据集、预训练模型和实用代码的综合性平台。\n\n面对中文 NLP 领域资源分散、入门门槛高以及特定场景数据匮乏的痛点，funNLP 提供了“一站式”解决方案。这里不仅涵盖了分词、命名实体识别、情感分析、文本摘要等基础任务的标准工具，还独特地收录了丰富的垂直领域资源，如法律、医疗、金融行业的专用词库与数据集，甚至包含古诗词生成、歌词创作等趣味应用。其核心亮点在于极高的全面性与实用性，从基础的字典词典到前沿的 BERT、GPT-2 模型代码，再到高质量的标注数据和竞赛方案，应有尽有。\n\n无论是刚刚踏入 NLP 领域的学生、需要快速验证想法的算法工程师，还是从事人工智能研究的学者，都能在这里找到急需的“武器弹药”。对于开发者而言，它能大幅减少寻找数据和复现模型的时间；对于研究者，它提供了丰富的基准测试资源和前沿技术参考。funNLP 以开放共享的精神，极大地降低了中文自然语言处理的开发与研究成本，是中文 AI 社区不可或缺的宝藏仓库。",79857,1,"2026-04-08T20:11:31",[19,14,18],{"id":33,"name":34,"github_repo":35,"description_zh":36,"stars":37,"difficulty_score":29,"last_commit_at":38,"category_tags":39,"status":22},5773,"cs-video-courses","Developer-Y\u002Fcs-video-courses","cs-video-courses 是一个精心整理的计算机科学视频课程清单，旨在为自学者提供系统化的学习路径。它汇集了全球知名高校（如加州大学伯克利分校、新南威尔士大学等）的完整课程录像，涵盖从编程基础、数据结构与算法，到操作系统、分布式系统、数据库等核心领域，并深入延伸至人工智能、机器学习、量子计算及区块链等前沿方向。\n\n面对网络上零散且质量参差不齐的教学资源，cs-video-courses 解决了学习者难以找到成体系、高难度大学级别课程的痛点。该项目严格筛选内容，仅收录真正的大学层级课程，排除了碎片化的简短教程或商业广告，确保用户能接触到严谨的学术内容。\n\n这份清单特别适合希望夯实计算机基础的开发者、需要补充特定领域知识的研究人员，以及渴望像在校生一样系统学习计算机科学的自学者。其独特的技术亮点在于分类极其详尽，不仅包含传统的软件工程与网络安全，还细分了生成式 AI、大语言模型、计算生物学等新兴学科，并直接链接至官方视频播放列表，让用户能一站式获取高质量的教育资源，免费享受世界顶尖大学的课堂体验。",79792,"2026-04-08T22:03:59",[18,13,14,20],{"id":41,"name":42,"github_repo":43,"description_zh":44,"stars":45,"difficulty_score":46,"last_commit_at":47,"category_tags":48,"status":22},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。",77062,3,"2026-04-04T04:44:48",[17,13,20,19,18],{"id":50,"name":51,"github_repo":52,"description_zh":53,"stars":54,"difficulty_score":46,"last_commit_at":55,"category_tags":56,"status":22},519,"PaddleOCR","PaddlePaddle\u002FPaddleOCR","PaddleOCR 是一款基于百度飞桨框架开发的高性能开源光学字符识别工具包。它的核心能力是将图片、PDF 等文档中的文字提取出来，转换成计算机可读取的结构化数据，让机器真正“看懂”图文内容。\n\n面对海量纸质或电子文档，PaddleOCR 解决了人工录入效率低、数字化成本高的问题。尤其在人工智能领域，它扮演着连接图像与大型语言模型（LLM）的桥梁角色，能将视觉信息直接转化为文本输入，助力智能问答、文档分析等应用场景落地。\n\nPaddleOCR 适合开发者、算法研究人员以及有文档自动化需求的普通用户。其技术优势十分明显：不仅支持全球 100 多种语言的识别，还能在 Windows、Linux、macOS 等多个系统上运行，并灵活适配 CPU、GPU、NPU 等各类硬件。作为一个轻量级且社区活跃的开源项目，PaddleOCR 既能满足快速集成的需求，也能支撑前沿的视觉语言研究，是处理文字识别任务的理想选择。",75309,"2026-04-10T11:12:54",[19,13,20,18],{"id":58,"name":59,"github_repo":60,"description_zh":61,"stars":62,"difficulty_score":29,"last_commit_at":63,"category_tags":64,"status":22},3215,"awesome-machine-learning","josephmisiti\u002Fawesome-machine-learning","awesome-machine-learning 是一份精心整理的机器学习资源清单，汇集了全球优秀的机器学习框架、库和软件工具。面对机器学习领域技术迭代快、资源分散且难以甄选的痛点，这份清单按编程语言（如 Python、C++、Go 等）和应用场景（如计算机视觉、自然语言处理、深度学习等）进行了系统化分类，帮助使用者快速定位高质量项目。\n\n它特别适合开发者、数据科学家及研究人员使用。无论是初学者寻找入门库，还是资深工程师对比不同语言的技术选型，都能从中获得极具价值的参考。此外，清单还延伸提供了免费书籍、在线课程、行业会议、技术博客及线下聚会等丰富资源，构建了从学习到实践的全链路支持体系。\n\n其独特亮点在于严格的维护标准：明确标记已停止维护或长期未更新的项目，确保推荐内容的时效性与可靠性。作为机器学习领域的“导航图”，awesome-machine-learning 以开源协作的方式持续更新，旨在降低技术探索门槛，让每一位从业者都能高效地站在巨人的肩膀上创新。",72149,"2026-04-03T21:50:24",[20,18],{"id":66,"github_repo":67,"name":68,"description_en":69,"description_zh":70,"ai_summary_zh":70,"readme_en":71,"readme_zh":72,"quickstart_zh":73,"use_case_zh":74,"hero_image_url":75,"owner_login":76,"owner_name":77,"owner_avatar_url":78,"owner_bio":79,"owner_company":80,"owner_location":81,"owner_email":82,"owner_twitter":82,"owner_website":82,"owner_url":83,"languages":82,"stars":84,"forks":85,"last_commit_at":86,"license":82,"difficulty_score":29,"env_os":87,"env_gpu":88,"env_ram":88,"env_deps":89,"category_tags":92,"github_topics":82,"view_count":10,"oss_zip_url":82,"oss_zip_packed_at":82,"status":22,"created_at":93,"updated_at":94,"faqs":95,"releases":96},6336,"jdlc105\u002FMust-read-papers-and-continuous-tracking-on-Graph-Neural-Network-GNN-progress","Must-read-papers-and-continuous-tracking-on-Graph-Neural-Network-GNN-progress","Papers on Graph neural network(GNN) ","Must-read-papers-and-continuous-tracking-on-Graph-Neural-Network-GNN-progress 是一个专注于图神经网络（GNN）领域的开源学术资源库。面对社交网络、生物信息、药物研发及智能交通等现实场景中日益复杂的图数据，该工具旨在解决研究人员在海量文献中难以快速定位核心成果与追踪前沿进展的痛点。\n\n它系统性地整理了 GNN 及其变体（如图卷积网络、图注意力网络等）的必读论文，并持续更新来自 ICML、KDD 等顶级会议的最新研究成果。项目不仅涵盖了从基础理论到多领域应用的综述文章，还特别记录了里程碑式工作的引用增长轨迹，直观反映技术热度与发展趋势。\n\n无论是刚入门的研究生，还是深耕该领域的资深科学家，都能从中高效获取关键资料，把握“几何深度学习”的发展脉络。通过提供结构化的文献列表和持续的动态追踪，Must-read-papers-and-continuous-tracking-on-Graph-Neural-Network-GNN-progress 成为了连接理论与应用、助力全球开发者推动图神经网络技术落地的重要桥梁。","# Must-read papers and continuous track on Graph Neural Network (GNN) progress\n\nMany important real-world applications and issues come in the form of graphs, such as social network, protein-protein interaction network, brain network, chemical molecular graph and 3D point cloud. Therefore, driven by the above interdisciplinary research, the neural network model for graph data has become an emerging research hotspot. ***GNN and its variants are an emerging and powerful neural network model. Its applications are no longer limited to the original field of social network. It has flourished in many other areas, such as Data Visualization, Image Processing, NLP, Recommendation System, Computer Vision, Bioinformatics, Chemical informatics, Drug Development and Discovery, Smart Transportation.*** This project focuses on GNN, which lists relevant must-read papers and keeps track of progress. Note that actual overall progress in GNN should include, but not be limited to, these papers. We look forward to promoting this direction and providing several helps to researchers in this direction.\n\nContributed by Allen Bluce (Bentian Li) and Anne Bluce (Yunxia Lin), If there is something wrong or GNN-related issue, welcome to send email (Address: jdlc105@qq.com, lbtjackbluce@gmail.com).\n\n***Technology Keyword: Graph Neural Network, Graph convolutional network, Graph network, Graph attention network, Graph auto-encoder, Graph convolutional reinforcement learning, Graph capsule neural network....***\n\n```diff\n+ Very hot research topic:\n```\n***The most representative work--Semi-supervised classification with graph convolutional networks (GCNs) proposed by T.N. Kipf and M. Welling (ICLR2017 [5] in conference paper list) has been cited 1,020 times in Google Scholar (on 09 May 2019).*** Update: 1, 065 times (on 20 May 2019); Update: 1, 106 times (on 27 May 2019); Update: 1, 227 times (on 19 June 2019); Update: 1, 377 times (on 8 July 2019); Update: 1, 678 times (on 17 Sept. 2019); Update: 1, 944 times (on 29 Oct. 2019); Update: 2, 232 times (on 9 Dec. 2019); Update: 2, 677 times (on 2 Feb. 2020).Update: 3, 018 times (on 17 March. 2020); Update: 3,560 times (on 27 May. 2020); Update: 4,060 times (on 3 July. 2020); Update: 5,371 times (on 25 Oct. 2020). Update: 6,258 times (on 01 Jan. 2021). Update: 6,672 times (on 07 Feb. 2021). Update: 8,454 times (on 16 June. 2021). Update: 14,251 times (on 21 April. 2022). **Update: 22,270 times (on 28 March 2023)**.\n\n***Project Start time: 11 Dec 2018, Latest updated time: 28 March 2023***. Thanks for giving us so many stars and supports from the developers and scientists on Github around the world！！！ We will continue to make this project better. \n\n```diff\n+ News: Recent Papers about GNN models and their applications have come from ICML2022, KDD2022,... We are waiting for more paper to be released.\n```\n\n\n## ![#f03c15](https:\u002F\u002Fvia.placeholder.com\u002F15\u002Ff03c15\u002F000000?text=+) `Survey papers`:\n\n1. Bronstein M M, Bruna J, LeCun Y, et al. **Geometric deep learning: going beyond euclidean data**. IEEE Signal Processing Magazine, 2017, 34(4): 18-42. [paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F7974879\u002F)\n\n2. Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Maosong Sun, **Graph Neural Networks: A Review of Methods and Applications**, ArXiv, 2018. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1812.08434.pdf).\n\n3. Battaglia P W, Hamrick J B, Bapst V, et al. **Relational inductive biases, deep learning, and graph networks**, arXiv 2018. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1806.01261.pdf)\n \n4. Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, Philip S. Yu(Fellow,IEEE), **A Comprehensive Survey on Graph Neural Networks**, IEEE Transactions on Neural Networks and Learning Systems, 2020. [paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9046288).\n  \n5. Ziwei Zhang, Peng Cui, Wenwu Zhu, **Deep Learning on Graphs: A Survey**, IEEE Transactions on Knowledge and Data Engineering, 2020. [paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9039675).\n\n6. Chen Z, Chen F, Zhang L, et al. **Bridging the Gap between Spatial and Spectral Domains: A Survey on Graph Neural Networks**. arXiv preprint. 2020. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2002.11867.pdf)\n \n7. Abadal S, Jain A, Guirado R, et al. **Computing Graph Neural Networks: A Survey from Algorithms to Accelerators**. arXiv preprint. 2020. [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.00130)\n\n8. Lamb L, Garcez A, Gori M, et al. **Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective**. arXiv preprint. 2020. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2003.00330.pdf)\n\n9. **Computing graph neural networks: A survey from algorithms to accelerators**. ACM Computing Surveys, 2021. [paper](https:\u002F\u002Fdl-acm-org.ilyvt.top\u002Fdoi\u002Fpdf\u002F10.1145\u002F3477141)\n\n9. **Survey on Graph Neural Network Acceleration: An Algorithmic Perspective**. IJCAI 2022. [paper](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F0772.pdf)\n\n9. **Graph neural networks in recommender systems: a survey**. ACM Computing Surveys, 2022. [paper](https:\u002F\u002Fdl-acm-org.ilyvt.top\u002Fdoi\u002Fpdf\u002F10.1145\u002F3535101)\n\n9. **Trustworthy graph neural networks: Aspects, methods and trends**. arXiv preprint, 2022. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2205.07424.pdf)\n\n9. **Explainability in Graph Neural Networks: A Taxonomic Survey**. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022. [paper](https:\u002F\u002Fieeexplore.m7h.net\u002Fabstract\u002Fdocument\u002F9875989)\n\n9. **Graph Lifelong Learning: A Survey**. IEEE Computational Intelligence Magazine, 2023. [paper](https:\u002F\u002Fieeexplore.m7h.net\u002Fabstract\u002Fdocument\u002F10026151)\n\n9. **A Comprehensive Survey of Graph-level Learning**. arXiv preprint, 2023. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2301.05860.pdf)\n\n9. **Self-Supervised Learning of Graph Neural Networks: A Unified Review**. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023. [paper](https:\u002F\u002Fieeexplore.m7h.net\u002Fdocument\u002F9764632)\n\n## ![#f03c15](https:\u002F\u002Fvia.placeholder.com\u002F15\u002Ff03c15\u002F000000?text=+) `Journal papers`:\n\n1. F. Scarselli, M. Gori, A.C. Tsoi, M. Hagenbuchner, G. Monfardini, **The graph neural network model**, IEEE Transactions on Neural Networks(IEEE Transactions on Neural Networks and Learning Systems), 2009. [paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=4700287&tag=1).\n\n2. Scarselli F, Gori M, Tsoi A C, et al. **Computational capabilities of graph neural networks**, IEEE Transactions on Neural Networks, 2009. [paper](http:\u002F\u002Fxueshu.baidu.com\u002Fs?wd=paperuri%3A%28805e5d5918a5146f2869293957ff0613%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fieeexplore.ieee.org%2Fdocument%2F4703190%2F&ie=utf-8&sc_us=16054091727677360492).\n\n3. Micheli A . **Neural Network for Graphs: A Contextual Constructive Approach**. IEEE Transactions on Neural Networks, 2009. [paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F4773279).\n\n4. Goles, Eric, and Gonzalo A. Ruz. **Dynamics of Neural Networks over Undirected Graphs**. Neural Networks, 2015. [paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0893608014002688).\n\n5. Z. Luo, L. Liu, J. Yin, Y. Li, Z. Wu, **Deep Learning of Graphs with Ngram Convolutional Neural Networks**, IEEE Transactions on Knowledge & Data Engineering, 2017. [paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7961216). [code](https:\u002F\u002Fgithub.com\u002Fzhilingluo\u002FNgramCNN).\n\n6. Petroski Such F , Sah S , Dominguez M A , et al. **Robust Spatial Filtering with Graph Convolutional Neural Networks**. IEEE Journal of Selected Topics in Signal Processing, 2017. [paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F7979525).\n\n7. Kawahara J, Brown C J, Miller S P, et al. **BrainNetCNN: convolutional neural networks for brain networks; towards predicting neurodevelopment**. NeuroImage, 2017. [paper](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1053811916305237).\n\n8. Muscoloni A , Thomas J M , Ciucci S , et al. **Machine learning meets complex networks via coalescent embedding in the hyperbolic space**. Nature Communications, 2017. [paper](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-017-01825-5).\n\n9. D.M. Camacho, K.M. Collins, R.K. Powers, J.C. Costello, J.J. Collins, **Next-Generation Machine Learning for Biological Networks**, Cell, 2018. [paper](https:\u002F\u002Fwww.cell.com\u002Fcell\u002Ffulltext\u002FS0092-8674(18)30592-0?rss=yes).\n\n10. Marinka Z , Monica A , Jure L . **Modeling polypharmacy side effects with graph convolutional networks**. Bioinformatics, 2018. 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[paper](https:\u002F\u002Fieeexplore-ieee-org.flyyouth.top\u002Fabstract\u002Fdocument\u002F9096540\u002F)\n\n44. Holzinger A, et al. **Towards multi-modal causability with Graph Neural Networks enabling information fusion for explainable AI**, Information Fusion, 2021. [paper](https:\u002F\u002Fwww-sciencedirect-com.cam.80599.net\u002Fscience\u002Farticle\u002Fpii\u002FS1566253521000142)\n\n45. Bianchi F M, et al. **Graph neural networks with convolutional arma filters**. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021. [paper](https:\u002F\u002Fieeexplore.7648.top\u002Fabstract\u002Fdocument\u002F9336270\u002F)\n\n46. Bentian Li, et al. **Dual Mutual Robust Graph Convolutional Network for Weakly Supervised Node Classification in Social Networks of Internet of People**. IEEE Internet of Things Journal, 2021. [paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9463468)\n\n47. Chowdhury A, et al. **Unfolding wmmse using graph neural networks for efficient power allocation**. IEEE Transactions on Wireless Communications, 2021. [paper](https:\u002F\u002Fieeexplore.7648.top\u002Fabstract\u002Fdocument\u002F9403959\u002F)\n\n### ![#1589F0](https:\u002F\u002Fvia.placeholder.com\u002F15\u002F1589F0\u002F000000?text=+) `Progress in 2022 Journal Papers`:\n----------------------------------------------------------------------------------------------------------------------------------------------------------\n**Novel GNN methods proposed in 2022** \n\n48. **Deep Constraint-Based Propagation in Graph Neural Networks**. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022. [paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9405452)\n\n50. **Learning Deep Graph Representations via Convolutional Neural Networks**. IEEE Transactions on Knowledge and Data Engineering, 2022. [paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9158338)\n\n51. **On Inductive–Transductive Learning With Graph Neural Networks**. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022. [paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9335498)\n\n---------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n**Novel GNN-based applications proposed in 2022** \n\n51. **A Graph Neural Network-Based Digital Twin for Network Slicing Management**. IEEE Transactions on Industrial Informatics, 2022. [paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9310275)\n\n53. **Low-Complexity Recruitment for Collaborative Mobile Crowdsourcing Using Graph Neural Networks**. IEEE Internet of Things Journal, 2022. [paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9446513)\n\n53. **Resilient UAV Swarm Communications With Graph Convolutional Neural Network**. IEEE Journal on Selected Areas in Communications, 2022. [paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9618911)\n\n53. **A Graph Neural Network Framework for Social Recommendations**. IEEE Transactions on Knowledge and Data Engineering, 2022. [paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9139346)\n\n### ![#1589F0](https:\u002F\u002Fvia.placeholder.com\u002F15\u002F1589F0\u002F000000?text=+) `Progress in 2023 Journal Papers`:\n----------------------------------------------------------------------------------------------------------------------------------------------------------\n**Novel GNN methods proposed in 2023** \n\n55. **Higher-Order Interaction Goes Neural: A Substructure Assembling Graph Attention Network for Graph Classification**. IEEE Transactions on Knowledge and Data Engineering, 2023. [paper](https:\u002F\u002Fieeexplore.m7h.net\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9516965)\n\n53. **Higher-Order Attribute-Enhancing Heterogeneous Graph Neural Networks**. IEEE Transactions on Knowledge and Data Engineering, 2023. [paper](https:\u002F\u002Fieeexplore.m7h.net\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9415142)\n\n53. **Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting**. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023. [paper](https:\u002F\u002Fieeexplore.m7h.net\u002Fdocument\u002F9721082)\n\n53. **Neighbor-Anchoring Adversarial Graph Neural Networks**. IEEE Transactions on Knowledge and Data Engineering, 2023. [paper](https:\u002F\u002Fieeexplore.m7h.net\u002Fdocument\u002F9453132)\n\n53. **HGNAS++: Efficient Architecture Search for Heterogeneous Graph Neural Networks**. IEEE Transactions on Knowledge and Data Engineering, 2023. [paper](https:\u002F\u002Fieeexplore.m7h.net\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=10040227)\n\n53. **Multi-View Tensor Graph Neural Networks Through Reinforced Aggregation**. IEEE Transactions on Knowledge and Data Engineering, 2023. [paper](https:\u002F\u002Fieeexplore.m7h.net\u002Fdocument\u002F9711926)\n\n53. **Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark Study**. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023. [paper](https:\u002F\u002Fieeexplore.m7h.net\u002Fdocument\u002F9773017\u002F)\n\n53. **Reinforced Causal Explainer for Graph Neural Networks**. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023. [paper](https:\u002F\u002Fieeexplore.m7h.net\u002Fdocument\u002F9763330)\n\n\n---------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n**Novel GNN-based applications proposed in 2023** \n\n63. **Combining Graph Neural Networks With Expert Knowledge for Smart Contract Vulnerability Detection**. IEEE Transactions on Knowledge and Data Engineering, 2023. [paper](https:\u002F\u002Fieeexplore.m7h.net\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9477066)\n\n53. **Bundle Recommendation and Generation With Graph Neural Networks**. IEEE Transactions on Knowledge and Data Engineering, 2023. [paper](https:\u002F\u002Fieeexplore.m7h.net\u002Fdocument\u002F9546546)\n\n53. **Integrating Multi-Label Contrastive Learning With Dual Adversarial Graph Neural Networks for Cross-Modal Retrieval**. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023. [paper](https:\u002F\u002Fieeexplore.m7h.net\u002Fdocument\u002F9815553)\n\n\n## ![#f03c15](https:\u002F\u002Fvia.placeholder.com\u002F15\u002Ff03c15\u002F000000?text=+) `Conference papers`:\n\n1. Duvenaud D, Maclaurin D, Aguilera-Iparraguirre J, et al. **Convolutional networks on graphs for learning molecular fingerprints**, NeurIPS(NIPS) 2015.  [paper](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F5954-convolutional-networks-on-graphs-for-learning-molecular-fingerprints.pdf). [code](https:\u002F\u002Fgithub.com\u002FHIPS\u002Fneural-fingerprint).\n\n2. M. Niepert, M. Ahmed, K. Kutzkov, **Learning Convolutional Neural Networks for Graphs**, ICML 2016. [paper](http:\u002F\u002Fproceedings.mlr.press\u002Fv48\u002Fniepert16.pdf).\n\n3. S. Cao, W. Lu, Q. Xu, **Deep neural networks for learning graph representations**, AAAI 2016. [paper](https:\u002F\u002Faaai.org\u002Focs\u002Findex.php\u002FAAAI\u002FAAAI16\u002Fpaper\u002Fview\u002F12423).\n\n4. M. Defferrard, X. Bresson, P. Vandergheynst, **Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering**, NeurIPS(NIPS) 2016. [paper](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F6081-convolutional-neural-networks-on-graphs-with-fast-localized-spectral-filtering.pdf). [code](https:\u002F\u002Fgithub.com\u002Fmdeff\u002Fcnn_graph).\n\n5. T.N. Kipf, M. Welling, **Semi-Supervised Classification with Graph Convolutional Networks**, ICLR 2017. [paper](https:\u002F\u002Fwww.ics.uci.edu\u002F~welling\u002Fpublications\u002Fpapers\u002FSubmitted2016-SSL-GCNN.pdf). [code](http:\u002F\u002Ftkipf.github.io\u002Fgraph-convolutional-networks\u002F).\n\n6. A. Fout, B. Shariat, J. Byrd, A. Benhur, **Protein Interface Prediction using Graph Convolutional Networks**, NeurIPS(NIPS) 2017. [paper](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F7231-protein-interface-prediction-using-graph-convolutional-networks).\n\n7. Monti F, Bronstein M, Bresson X. **Geometric matrix completion with recurrent multi-graph neural networks**, NeurIPS(NIPS) 2017. [paper](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F6960-geometric-matrix-completion-with-recurrent-multi-graph-neural-networks.pdf).\n\n8. Simonovsky M, Komodakis N. **Dynamic edgeconditioned filters in convolutional neural networks on graphs**, CVPR. 2017. [paper](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_cvpr_2017\u002Fpapers\u002FSimonovsky_Dynamic_Edge-Conditioned_Filters_CVPR_2017_paper.pdf)\n\n9. R. Li, S. Wang, F. Zhu, J. Huang, **Adaptive Graph Convolutional Neural Networks**, AAAI 2018. [paper](https:\u002F\u002Fwww.aaai.org\u002Focs\u002Findex.php\u002FAAAI\u002FAAAI18\u002Fpaper\u002Fview\u002F16642)\n\n10. J. You, B. Liu, R. Ying, V. Pande, J. Leskovec, **Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation**, NeurIPS(NIPS) 2018.  [paper](http:\u002F\u002Fxueshu.baidu.com\u002Fs?wd=paperuri%3A%28dd21a587e909dfc3a0159ca428d3580a%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Farxiv.org%2Fpdf%2F1806.02473&ie=utf-8&sc_us=3351961245348982134&sc_as_para=sc_lib%3A).\n\n11. C. Zhuang, Q. Ma, **Dual Graph Convolutional Networks for Graph-Based Semi-Supervised Classification**, WWW 2018. [paper](https:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?doid=3178876.3186116)\n\n12. H. Gao, Z. Wang, S. Ji, **Large-Scale Learnable Graph Convolutional Networks**, KDD 2018. [paper](https:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?doid=3219819.3219947)\n\n13. D. Zügner, A. Akbarnejad, S. Günnemann, **Adversarial Attacks on Neural Networks for Graph Data**, KDD 2018. [paper](https:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?doid=3219819.3220078)\n\n14. Ying R , He R , Chen K , et al. **Graph Convolutional Neural Networks for Web-Scale Recommender Systems**. KDD 2018.  [paper](https:\u002F\u002Fcs.stanford.edu\u002Fpeople\u002Fjure\u002Fpubs\u002Fpinsage-kdd18.pdf)\n\n15. P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò, Y. Bengio, **Graph Attention Networks**, ICLR, 2018. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1710.10903.pdf)\n\n16. Beck, Daniel Edward Robert, Gholamreza Haffari and Trevor Cohn. **Graph-to-Sequence Learning using Gated Graph Neural Networks.** ACL 2018. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1806.09835.pdf)\n\n17. Yu B, Yin H, Zhu Z. **Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting**. IJCAI 2018. [paper](https:\u002F\u002Farxiv.xilesou.top\u002Fpdf\u002F1709.04875.pdf)\n\n18. Chen J , Zhu J , Song L . **Stochastic Training of Graph Convolutional Networks with Variance Reduction**. ICML 2018. [paper](http:\u002F\u002Fproceedings.mlr.press\u002Fv80\u002Fchen18p.html)\n\n19. Gusi Te, Wei Hu, Amin Zheng, Zongming Guo, **RGCNN: Regularized Graph CNN for Point Cloud Segmentation**. ACM Multimedia 2018. [paper](http:\u002F\u002Fwww.icst.pku.edu.cn\u002FF\u002Fintro\u002Fhuwei\u002Findex_files\u002Fpapers\u002FACMMM18_Te.pdf), [code](https:\u002F\u002Fgithub.com\u002Ftegusi\u002FRGCNN), \n\n20. Talukdar, Partha, Shikhar Vashishth, Shib Sankar Dasgupta and Swayambhu Nath Ray. **Dating Documents using Graph Convolution Networks.** ACL 2018. 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[paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.11960)\n\n221. Liang Yang, et al. **Why Do Attributes Propagate in Graph Convolutional Neural Networks?**. AAAI 2021, [paper](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F16588)\n\n222. Xueyang Fu, et al. **Rain Streak Removal via Dual Graph Convolutional Network**. AAAI 2021, [paper](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F16224)\n\n223. Inhwan Bae et al. **Disentangled Multi-Relational Graph Convolutional Network for Pedestrian Trajectory Prediction**. AAAI 2021, [paper](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F16174)\n\n224. Xin Xia et al. **Self-Supervised Hypergraph Convolutional Networks for Session-Based Recommendation**. AAAI 2021, [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.06852)\n\n225. Sheng Wan et al. **Contrastive and Generative Graph Convolutional Networks for Graph-Based SemiSupervised Learning**. AAAI 2021, [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.07111)\n\n226. Zhan Chen et al. **Multi-Scale Spatial Temporal Graph Convolutional Network for Skeleton-Based Action Recognition**. AAAI 2021, [paper](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F16197)\n\n227. Xin Chen et al. **Fitting the Search Space of Weight-Sharing NAS with Graph Convolutional Networks**. AAAI 2021, [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2004.08423)\n\n228. Qingbao Huang et al. **Story Ending Generation with Multi-Level Graph Convolutional Networks over Dependency Trees**. AAAI 2021, [paper](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F17545)\n\n229. Heng Chang et al. **Power up! Robust Graph Convolutional Network via Graph Powering**. AAAI 2021, [paper](https:\u002F\u002Fpeople.eecs.berkeley.edu\u002F~sojoudi\u002FRobust_GCN.pdf)\n\n230. Deyu Bo et al. **Beyond Low-Frequency Information in Graph Convolutional Networks**. AAAI 2021, [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2101.00797)\n\n231. Han Yang et al. **Rethinking Graph Regularization for Graph Neural Networks**. AAAI 2021, [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.02027)\n\n232. Tong Zhao et al. **Data Augmentation for Graph Neural Networks**. AAAI 2021, [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.06830)\n\n233. Jiaxuan You et al. **Identity-Aware Graph Neural Networks**. AAAI 2021, [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2101.10320)\n\n234. Yuanfu Lu et al. **Learning to Pre-Train Graph Neural Networks**. AAAI 2021, [paper](http:\u002F\u002Fshichuan.org\u002Fdoc\u002F101.pdf)\n\n235. Q Li et al. **Learning Graph Neural Networks with Approximate Gradient Descent**. AAAI 2021, [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.03429)\n\n236. Yuankai Wu et al. **Inductive Graph Neural Networks for Spatiotemporal Kriging**. AAAI 2021, [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.07527)\n\n237. Jiong Zhu et al. **Graph Neural Networks with Heterophily**. AAAI 2021, [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.13566)\n\n238. Mengzhang Li et al. **Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting**. AAAI 2021, [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.09641)\n\n239. Shengsheng Qian et al. **Dual Adversarial Graph Neural Networks for Multi-Label Cross-Modal Retrieval**. AAAI 2021, [paper](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F16345)\n\n240. Mengzhang Li et al. **Graph Neural Network-Based Anomaly Detection in Multivariate Time Series**. AAAI 2021, [paper](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F16523)\n\n241. Fan Zhou et al. **Overcoming Catastrophic Forgetting in Graph Neural Networks with Experience Replay**. AAAI 2021, [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.06002)\n\n242. Georgios Panagopoulos et al. **Transfer Graph Neural Networks for Pandemic Forecasting**. AAAI 2021, [paper](https:\u002F\u002Fwww.researchgate.net\u002Fpublication\u002F344294392_Transfer_Graph_Neural_Networks_for_Pandemic_Forecasting)\n\n243. Uday Shankar Shanthamallu et al. **Uncertainty-Matching Graph Neural Networks to Defend against Poisoning Attacks**. AAAI 2021, [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.14455)\n\n244. Jianan Zhao et al. **Heterogeneous Graph Structure Learning for Graph Neural Networks**. AAAI 2021, [paper](http:\u002F\u002Fshichuan.org\u002Fdoc\u002F100.pdf)\n\n245. Yanan Zhang et al. **PC-RGNN: Point Cloud Completion and Graph Neural Network for 3D Object Detection**. AAAI 2021, [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.10412)\n\n246. Tengfei Song et al. **Uncertain Graph Neural Networks for Facial Action Unit Detection**. AAAI 2021, [paper](https:\u002F\u002Fwww.researchgate.net\u002Fpublication\u002F346853340_Uncertain_Graph_Neural_Networks_for_Facial_Action_Unit_Detection)\n\n247. Li Sun et al. **Hyperbolic Variational Graph Neural Network for Modeling Dynamic Graphs**. AAAI 2021, [paper](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F16563))\n\n248. Binghui Wang et al. **Semi-Supervised Node Classification on Graphs: Markov Random Fields vs. Graph Neural Networks**. AAAI 2021, [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.13085)\n\n249. Arijit Sehanobish et al. **Gaining Insight into SARS-CoV-2 Infection and COVID-19 Severity Using Self-Supervised Edge Features and Graph Neural Networks**. AAAI 2021, [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.12971)\n\n250. Utkarsh Desai et al. **Graph Neural Network to Dilute Outliers for Refactoring Monolith Application**. AAAI 2021, [paper](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F16079))\n\n251. Huihui Liu et al. **Overcoming Catastrophic Forgetting in Graph Neural Networks**. AAAI 2021, [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.06002)\n\n252. Yuhang Yao et al. **Interpretable Clustering on Dynamic Graphs with Recurrent Graph Neural Networks**. AAAI 2021, [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.08740)\n\n253. Daizong Liu et al. **Spatiotemporal Graph Neural Network Based Mask Reconstruction for Video Object Segmentation**. AAAI 2021, [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.05499)\n\n254. Cai T et al. **Graphnorm: A principled approach to accelerating graph neural network training**. ICML 2021. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2009.03294.pdf)\n\n255. Baranwal A et al. **Graph Convolution for Semi-Supervised Classification: Improved Linear Separability and Out-of-Distribution Generalization**. ICML 2021. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2102.06966.pdf)\n\n256. Hang M et al. **A Collective Learning Framework to Boost GNN Expressiveness**. ICML 2021. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2003.12169.pdf)\n\n257. Henderson R et al. **Improving Molecular Graph Neural Network Explainability with Orthonormalization and Induced Sparsity**. ICML 2021. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2105.04854.pdf)\n\n258. Fey M et al. **GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings**. ICML 2021. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2106.05609.pdf)\n\n259. Guo-Sen Xie et al. **Scale-Aware Graph Neural Network for Few-Shot Semantic Segmentation**. CVPR 2021. [paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fpapers\u002FXie_Scale-Aware_Graph_Neural_Network_for_Few-Shot_Semantic_Segmentation_CVPR_2021_paper.pdf)\n\n260. Kai Fischer et al. **StickyPillars: Robust and Efficient Feature Matching on Point Clouds Using Graph Neural Networks**. CVPR 2021. [paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fpapers\u002FFischer_StickyPillars_Robust_and_Efficient_Feature_Matching_on_Point_Clouds_Using_CVPR_2021_paper.pdf)\n\n261. Yiding Yang et al. **Learning Dynamics via Graph Neural Networks for Human Pose Estimation and Tracking**. CVPR 2021. [paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fpapers\u002FYang_Learning_Dynamics_via_Graph_Neural_Networks_for_Human_Pose_Estimation_CVPR_2021_paper.pdf)\n\n262. Guillaume Jaume et al. **Quantifying Explainers of Graph Neural Networks in Computational Pathology**. CVPR 2021. [paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fpapers\u002FJaume_Quantifying_Explainers_of_Graph_Neural_Networks_in_Computational_Pathology_CVPR_2021_paper.pdf)\n\n263. Shaofei Cai, et al. **Rethinking Graph Neural Architecture Search From Message-Passing**. CVPR 2021. [paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fpapers\u002FCai_Rethinking_Graph_Neural_Architecture_Search_From_Message-Passing_CVPR_2021_paper.pdf)\n\n264. Yongcheng Jing, et al. **Amalgamating Knowledge From Heterogeneous Graph Neural Networks**. CVPR 2021. [paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fpapers\u002FJing_Amalgamating_Knowledge_From_Heterogeneous_Graph_Neural_Networks_CVPR_2021_paper.pdf)\n\n265. Mehdi Bahri, et al. **Binary Graph Neural Networks**. CVPR 2021. [paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fpapers\u002FBahri_Binary_Graph_Neural_Networks_CVPR_2021_paper.pdf)\n\n266. Liushuai Shi, et al. **SGCN: Sparse Graph Convolution Network for Pedestrian Trajectory Prediction**. CVPR 2021. [paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fpapers\u002FShi_SGCN_Sparse_Graph_Convolution_Network_for_Pedestrian_Trajectory_Prediction_CVPR_2021_paper.pdf)\n\n267. Dongyu She, et al. **Hierarchical Layout-Aware Graph Convolutional Network for Unified Aesthetics Assessment**. CVPR 2021. [paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fpapers\u002FShe_Hierarchical_Layout-Aware_Graph_Convolutional_Network_for_Unified_Aesthetics_Assessment_CVPR_2021_paper.pdf)\n\n268. Jindou Dai, et al. **A Hyperbolic-to-Hyperbolic Graph Convolutional Network**. CVPR 2021. [paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fpapers\u002FDai_A_Hyperbolic-to-Hyperbolic_Graph_Convolutional_Network_CVPR_2021_paper.pdf)\n\n269. Junfu Wang, et al. **Bi-GCN: Binary Graph Convolutional Network**. CVPR 2021. [paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fpapers\u002FWang_Bi-GCN_Binary_Graph_Convolutional_Network_CVPR_2021_paper.pdf)\n\n270. Razvan Caramalau, et al. **Sequential Graph Convolutional Network for Active Learning**. CVPR 2021. [paper](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fpapers\u002FCaramalau_Sequential_Graph_Convolutional_Network_for_Active_Learning_CVPR_2021_paper.pdf)\n\n271. Keyulu Xu, et al. **How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks**. ICLR 2021. [paper](https:\u002F\u002Fopenreview.net\u002Fpdf?id=UH-cmocLJC)\n\n272. Waiss Azizian, et al. **Expressive Power of Invariant and Equivariant Graph Neural Networks**. ICLR 2021. [paper](https:\u002F\u002Fopenreview.net\u002Fpdf?id=lxHgXYN4bwl)\n\n### ![#1589F0](https:\u002F\u002Fvia.placeholder.com\u002F15\u002F1589F0\u002F000000?text=+) `Progress in 2022 Conference Papers`:\n----------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n**Novel GNN methods proposed in 2022** \n\n274. **Learning from the Dark: Boosting Graph Convolutional Neural Networks with Diverse Negative Samples**. AAAI 2022. [paper](https:\u002F\u002Fwww.aaai.org\u002FAAAI22Papers\u002FAAAI-4442.DuanW.pdf)\n\n274. **Block Modeling-Guided Graph Convolutional Neural Networks**. AAAI 2022. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2112.13507.pdf)\n\n275. **Deformable Graph Convolutional Networks**. AAAI 2022. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2112.14438.pdf)\n\n276. **ProtGNN: Towards Self-Explaining Graph Neural Networks**. AAAI 2022. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2112.00911.pdf)\n\n277. **Adaptive Kernel Graph Neural Network**. AAAI 2022. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2112.04575.pdf)\n\n278. **Self-supervised Graph Neural Networks via Diverse and Interactive Message Passing**. AAAI 2022. [paper](https:\u002F\u002Fyangliang.github.io\u002Fpdf\u002Faaai22.pdf)\n\n279. **A Self-Supervised Mixed-Curvature Graph Neural Network**. AAAI 2022. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2112.05393.pdf)\n\n280. **KerGNNs: Interpretable Graph Neural Networks with Graph Kernels**. AAAI 2022. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2201.00491.pdf)\n\n281. **Orthogonal Graph Neural Networks**. AAAI 2022. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2109.11338.pdf)\n\n282. **SAIL: Self-Augmented Graph Contrastive Learning**. AAAI 2022. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2009.00934.pdf)\n\n283. **AutoGCL: Automated Graph Contrastive Learning via Learnable View Generators**. AAAI 2022. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2109.10259.pdf)\n\n284. **Adversarial Graph Contrastive Learning with Information Regularization**. WWW 2022. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.06491.pdf)\n\n285. **Confidence May Cheat: Self-Training on Graph Neural Networks under Distribution Shift**. WWW 2022. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2201.11349.pdf)\n\n286. **Curvature Graph Generative Adversarial Networks**. WWW 2022. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.01604.pdf)\n\n287. **Dual Space Graph Contrastive Learning**. WWW 2022. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2201.07409.pdf)\n\n288. **GBK-GNN: Gated Bi-Kernel Graph Neural Network for Modeling Both Homophily and Heterophily**. WWW 2022. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2110.15777.pdf)\n\n289. **Geometric Graph Representation Learning via Maximizing Rate Reduction**. WWW 2022. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.06241.pdf)\n\n290. **Graph Communal Contrastive Learning**. WWW 2022. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2110.14863.pdf)\n\n291. **Graph Neural Networks Beyond Compromise Between Attribute and Topology**. WWW 2022. [paper](https:\u002F\u002Fyangliang.github.io\u002Fpdf\u002Fwww22.pdf)\n\n292. **Graph-adaptive Rectified Linear Unit for Graph Neural Networks**. WWW 2022. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.06281.pdf)\n\n293. **Meta-Weight Graph Neural Network: Push the Limits Beyond Global Homophily**. WWW 2022. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.06281.pdf)\n\n294. **Polarized Graph Neural Networks**. WWW 2022. [Temporarily unavailable]\n\n295. **On Size-Oriented Long-Tailed Graph Classification of Graph Neural Networks**. WWW 2022. [paper](https:\u002F\u002Fzemin-liu.github.io\u002Fpapers\u002FSOLT-GNN-WWW-22.pdf)\n\n296. **SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation**. WWW 2022. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.03104.pdf)\n\n297. **Towards Unsupervised Deep Graph Structure Learning**. WWW 2022. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2201.06367.pdf)\n\n298. **Expressiveness and Approximation Properties of Graph Neural Networks**. ICLR 2022. [paper](https:\u002F\u002Fopenreview.net\u002Fpdf?id=wIzUeM3TAU)\n\n299. **A New Perspective on \"How Graph Neural Networks Go Beyond Weisfeiler-Lehman?\"**. ICLR 2022. [paper](https:\u002F\u002Fopenreview.net\u002Fpdf?id=uxgg9o7bI_3)\n\n299. **p-Laplacian Based Graph Neural Networks**. ICML 2022. [paper](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Ffu22e\u002Ffu22e.pdf)\n\n299. **Going Deeper into Permutation-Sensitive Graph Neural Networks**. ICML 2022. [paper](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fhuang22l\u002Fhuang22l.pdf)\n\n299. **SE(3) Equivariant Graph Neural Networks with Complete Local Frames**. ICML 2022. [paper](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fdu22e\u002Fdu22e.pdf)\n\n299. **A New Perspective on the Effects of Spectrum in Graph Neural Networks**. ICML 2022. [paper](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fyang22n\u002Fyang22n.pdf)\n\n299. **How Powerful are Spectral Graph Neural Networks**. ICML 2022. [paper](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fwang22am\u002Fwang22am.pdf)\n\n299. **Local Augmentation for Graph Neural Networks**. ICML 2022. [paper](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fliu22s\u002Fliu22s.pdf)\n\n299. **Graph Neural Network Training and Data Tiering**. KDD 2022. [paper](https:\u002F\u002Fdl-acm-org.ndfpz.top\u002Fdoi\u002Fpdf\u002F10.1145\u002F3534678.3539038)\n\n299. **Model Degradation Hinders Deep Graph Neural Networks**. KDD 2022. [paper](https:\u002F\u002Fdl-acm-org.geray.top\u002Fdoi\u002Fpdf\u002F10.1145\u002F3534678.3539374)\n\n299. **Improving Social Network Embedding via New Second-Order Continuous Graph Neural Networks**. KDD 2022. [paper](https:\u002F\u002Fdl-acm-org.geray.top\u002Fdoi\u002Fpdf\u002F10.1145\u002F3534678.3539415)\n\n299. **Graph Neural Networks with Node-wise Architecture**. KDD 2022. [paper](https:\u002F\u002Fdl-acm-org.geray.top\u002Fdoi\u002Fpdf\u002F10.1145\u002F3534678.3539387)\n\n299. **GPPT: Graph Pre-training and Prompt Tuning to Generalize Graph Neural Networks**. KDD 2022. [paper](https:\u002F\u002Fdl-acm-org.geray.top\u002Fdoi\u002Fpdf\u002F10.1145\u002F3534678.3539249)\n\n299. **How does Heterophily Impact the Robustness of Graph Neural Networks? Theoretical Connections and Practical Implications**. KDD 2022. [paper](https:\u002F\u002Fdl-acm-org.geray.top\u002Fdoi\u002Fpdf\u002F10.1145\u002F3534678.3539418)\n\n299. **Improving Fairness in Graph Neural Networks via Mitigating Sensitive Attribute Leakage**. KDD 2022. [paper](https:\u002F\u002Fdl-acm-org.ndfpz.top\u002Fdoi\u002Fpdf\u002F10.1145\u002F3534678.3539404)\n\n299. **Hierarchical Diffusion Scattering Graph Neural Network**. IJCAI 2022. [paper](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F0519.pdf)\n\n299. **RAW-GNN: RAndom Walk Aggregation based Graph Neural Network**. IJCAI 2022. [paper](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F0293.pdf)\n\n299. **Survey on Graph Neural Network Acceleration: An Algorithmic Perspective**. IJCAI 2022. [paper](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F0772.pdf)\n\n299. **Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification**. IJCAI 2022. [paper](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F0272.pdf)\n\n\n\n\n\n\n----------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n**Novel GNN-based applications proposed in 2022** \n\n318. **Hybrid Graph Neural Networks for Few-Shot Learning**. AAAI 2022. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2112.06538.pdf)\n\n299. **Qubit Routing Using Graph Neural Network Aided Monte Carlo Tree Search**. AAAI 2022. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2104.01992.pdf)\n\n299. **CausalGNN: Causal-Based Graph Neural Networks for Spatio-Temporal Epidemic Forecasting**. AAAI 2022. [paper](https:\u002F\u002Fwww.aaai.org\u002FAAAI22Papers\u002FAISI-6475.WangL.pdf)\n\n299. **LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks**. AAAI 2022. [paper](https:\u002F\u002Fwww.aaai.org\u002FAAAI22Papers\u002FAAAI-51.GoodgeA.pdf)\n\n299. **DDGCN: Dual Dynamic Graph Convolutional Networks for Rumor Detection on Social Media**. AAAI 2022. [paper](https:\u002F\u002Fwww.aaai.org\u002FAAAI22Papers\u002FAAAI-6370.SunM.pdf)\n\n299. **Low-Pass Graph Convolutional Network for Recommendation**. AAAI 2022. [paper](https:\u002F\u002Fwww.aaai.org\u002FAAAI22Papers\u002FAAAI-3643.WenhuiY.pdf)\n\n299. **Learning to Detect 3D Facial Landmarks via Heatmap Regression with Graph Convolutional Network**. AAAI 2022. [paper](https:\u002F\u002Fwww.aaai.org\u002FAAAI22Papers\u002FAAAI-4116.WangY.pdf)\n\n299. **GeomGCL: Geometric Graph Contrastive Learning for Molecular Property Prediction**. AAAI 2022. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2109.11730.pdf)\n\n299. **AUC-oriented Graph Neural Network for Fraud Detection**. WWW 2022. [paper](https:\u002F\u002Fponderly.github.io\u002Fpub\u002FAOGNN_WWW2022.pdf)\n\n299. **Molecular Representation Learning via Heterogeneous Motif Graph Neural Networks**. ICML 2022. [paper](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fyu22a\u002Fyu22a.pdf)\n\n299. **Rethinking Graph Neural Networks for Anomaly Detection**. ICML 2022. [paper](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Ftang22b\u002Ftang22b.pdf)\n\n299. **DSTAGNN: Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic Flow Forecasting**. ICML 2022. [paper](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Flan22a\u002Flan22a.pdf)\n\n299. **Motif Prediction with Graph Neural Networks**. KDD 2022. [paper](https:\u002F\u002Fdl-acm-org.nhobg.top\u002Fdoi\u002Fpdf\u002F10.1145\u002F3534678.3539343)\n\n299. **Graph Neural Networks in Life Sciences: Opportunities and Solutions**. KDD 2022. [paper](https:\u002F\u002Fdl-acm-org.nhobg.top\u002Fdoi\u002Fpdf\u002F10.1145\u002F3534678.3542628)\n\n299. **Graph2Route: A Dynamic Spatial-Temporal Graph Neural Network for Pick-up and Delivery Route Prediction**. KDD 2022. [paper](https:\u002F\u002Fdl-acm-org.ndfpz.top\u002Fdoi\u002Fpdf\u002F10.1145\u002F3534678.3539084)\n\n299. **Recipe2Vec: Multi-modal Recipe Representation Learning with Graph Neural Networks**. IJCAI 2022. [paper](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F0482.pdf)\n\n299. **Self-supervised Graph Neural Networks for Multi-behavior Recommendation**. IJCAI 2022. [paper](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F0285.pdf)\n\n299. **RouteNet-Erlang: A Graph Neural Network for Network Performance Evaluation**. IEEE INFORCOM 2022. [paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9796944)\n\n299. **PolyWorld: Polygonal Building Extraction with Graph Neural Networks in Satellite Images**. CVPR 2022. [paper](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F9880425)\n\n\n\n\n## ![#f03c15](https:\u002F\u002Fvia.placeholder.com\u002F15\u002Ff03c15\u002F000000?text=+) `ArXiv papers`:\n\n\n1. Li Y, Tarlow D, Brockschmidt M, et al. **Gated graph sequence neural networks**. arXiv 2015. [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1511.05493)\n\n2. Henaff M, Bruna J, LeCun Y. **Deep convolutional networks on graph-structured data**, arXiv 2015. [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1506.05163)\n\n3. Hechtlinger Y, Chakravarti P, Qin J. **A generalization of convolutional neural networks to graph-structured data**. arXiv 2017. [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1704.08165)\n\n4. Marcheggiani D, Titov I. **Encoding sentences with graph convolutional networks for semantic role labeling**. arXiv 2017. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1703.04826.pdf)\n\n5. Battaglia P W, Hamrick J B, Bapst V, et al. **Relational inductive biases, deep learning, and graph networks**, arXiv 2018. [paper](http:\u002F\u002Fxueshu.baidu.com\u002Fs?wd=paperuri%3A%28965a67685f0a5180ada2c6adaa15dc38%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Farxiv.org%2Fpdf%2F1806.01261&ie=utf-8&sc_us=8121954972779087681&sc_as_para=sc_lib%3A)\n\n6. Verma S, Zhang Z L. **Graph Capsule Convolutional Neural Networks**. arXiv 2018. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1805.08090.pdf)\n\n7. Zhang T , Zheng W , Cui Z , et al. **Tensor graph convolutional neural network**. arXiv 2018. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1803.10071.pdf)\n\n8. Zou D, Lerman G. **Graph Convolutional Neural Networks via Scattering**.  arXiv 2018. [paper](http:\u002F\u002Fxueshu.baidu.com\u002Fs?wd=paperuri%3A%287cdb7eb72b8fb59d6b831fe7b9dd3cc6%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Farxiv.org%2Fpdf%2F1804.00099&ie=utf-8&sc_us=16682575734158616085&sc_as_para=sc_lib%3A)\n\n9. Du J , Zhang S , Wu G , et al. **Topology Adaptive Graph Convolutional Networks**. arXiv 2018. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1710.10370.pdf).\n\n10. Shang C , Liu Q , Chen K S , et al. **Edge Attention-based Multi-Relational Graph Convolutional Networks**. arXiv 2018. [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1802.04944).\n\n11. Scardapane S , Vaerenbergh S V , Comminiello D , et al. **Improving Graph Convolutional Networks with Non-Parametric Activation Functions**. arXiv 2018. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1802.09405.pdf).\n\n12. Wang Y , Sun Y , Liu Z , et al. **Dynamic Graph CNN for Learning on Point Clouds**. arXiv 2018. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1801.07829.pdf).\n\n13. Ryu S , Lim J , Hong S H , et al. **Deeply learning molecular structure-property relationships using attention- and gate-augmented graph convolutional network**. arXiv 2018. [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.10988).\n\n14. Cui Z , Henrickson K , Ke R , et al. **High-Order Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting**. arXiv 2018. [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1802.07007).\n\n15. Shchur O , Mumme M , Bojchevski A , et al. **Pitfalls of Graph Neural Network Evaluation**. arXiv 2018. [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1811.05868).\n\n16. Bai Y , Ding H , Bian S , et al. **Graph Edit Distance Computation via Graph Neural Networks**. arXiv 2018. [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.05689).\n\n17. Pedro H. C. Avelar, Henrique Lemos, Marcelo O. R. Prates, Luis Lamb, **Multitask Learning on Graph Neural Networks - Learning Multiple Graph Centrality Measures with a Unified Network**.  arXiv 2018. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1809.07695.pdf).\n\n18. Matthew Baron, **Topology and Prediction Focused Research on Graph Convolutional Neural Networks**.  arXiv 2018. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1808.07769.pdf).\n\n19. Wenting Zhao, Chunyan Xu, Zhen Cui, Tong Zhang, Jiatao Jiang, Zhenyu Zhang, Jian Yang, **When Work Matters: Transforming Classical Network Structures to Graph CNN**. arXiv 2018. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1807.02653.pdf).\n\n20. Xavier Bresson, Thomas Laurent, **Residual Gated Graph ConvNets**. arXiv 2018. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1711.07553.pdf). \n\n21. Kun XuLingfei WuZhiguo WangYansong FengVadim Sheinin, **Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks**. arXiv 2018. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1804.00823.pdf). \n\n22. Xiaojie GuoLingfei WuLiang Zhao. **Deep Graph Translation**. arXiv 2018. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1805.09980.pdf). \n\n21. Choma, Nicholas, et al. **Graph Neural Networks for IceCube Signal Classification.** ArXiv 2018. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1809.06166.pdf).\n\n22. Tyler Derr, Yao Ma, Jiliang Tang. **Signed Graph Convolutional Network** ArXiv 2018. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1809.06166.pdf).\n\n23. Yawei Luo, Tao Guan, Junqing Yu, Ping Liu, Yi Yang. **Every Node Counts: Self-Ensembling Graph Convolutional Networks for Semi-Supervised Learning** ArXiv 2018. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1809.09925.pdf).\n\n24. Sun K, Koniusz P, Wang J. **Fisher-Bures Adversary Graph Convolutional Networks**. arXiv 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1903.04154.pdf).\n\n25. Kazi A, Burwinkel H, Vivar G, et al. **InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction**. arXiv 2019. [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1903.04233).\n\n26. Lemos H, Prates M, Avelar P, et al. **Graph Colouring Meets Deep Learning: Effective Graph Neural Network Models for Combinatorial Problems**. arXiv 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1903.04598.pdf).\n\n27. Diehl F, Brunner T, Le M T, et al. **Graph Neural Networks for Modelling Traffic Participant Interaction**. arXiv 2019. [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1903.01254).\n\n28. Murphy R L, Srinivasan B, Rao V, et al. **Relational Pooling for Graph Representations**. arXiv 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1903.02541.pdf).\n\n29. Zhang W, Shu K, Liu H, et al. **Graph Neural Networks for User Identity Linkage**. arXiv 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1903.02174.pdf).\n\n30. Ruiz L, Gama F, Ribeiro A. **Gated Graph Convolutional Recurrent Neural Networks**. arXiv 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1903.01888.pdf).\n\n31. Phillips S, Daniilidis K. **All Graphs Lead to Rome: Learning Geometric and Cycle-Consistent Representations with Graph Convolutional Networks**. arXiv 2019. [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1901.02078).\n\n32. Hu F, Zhu Y, Wu S, et al. **Semi-supervised Node Classification via Hierarchical Graph Convolutional Networks**. arXiv 2019. [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1902.06667).\n\n33. Deng Z, Dong Y, Zhu J. **Batch Virtual Adversarial Training for Graph Convolutional Networks**. arXiv 2019. [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1902.09192).\n\n34. Chen Z M, Wei X S, Wang P, et al.**Multi-Label Image Recognition with Graph Convolutional Networks**. arXiv 2019. [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1904.03582).\n\n35. Mallea M D G, Meltzer P, Bentley P J. **Capsule Neural Networks for Graph Classification using Explicit Tensorial Graph Representations**. arXiv 2019.  [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1902.08399.pdf).\n\n36. Peter Meltzer, Marcelo Daniel Gutierrez Mallea and Peter J. Bentley. **PiNet: A Permutation Invariant Graph Neural Network for Graph Classification**. arXiv 2019.  [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.03046.pdf).\n\n37. Padraig Corcoran. **Function Space Pooling For Graph Convolutional Networks**. arXiv 2019.  [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.06259.pdf).\n\n38. Sbastien Lerique, Jacob Levy Abitbol, and Mrton Karsai. **Joint embedding of structure and features via graph convolutional networks**. arXiv 2019.  [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.08636.pdf).\n\n39. Chen D, Lin Y, Li W, et al. **Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View**. arXiv 2019. [paper](https:\u002F\u002Farxiv.gg363.site\u002Fabs\u002F1909.03211)\n\n40. Ohue M, Ii R, Yanagisawa K, et al. **Molecular activity prediction using graph convolutional deep neural network considering distance on a molecular graph**. arXiv 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1907.01103.pdf).\n\n41. Gao X, Xiong H, Frossard P. **iPool--Information-based Pooling in Hierarchical Graph Neural Networks**. arXiv 2019. [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1907.00832.pdf).\n\n42. Zhou K, Song Q, Huang X, et al. **Auto-GNN: Neural Architecture Search of Graph Neural Networks**. arXiv 2019.  [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1909.03184.pdf).\n\n43. Vijay Prakash Dwivedi, et al. **Benchmarking Graph Neural Networks**. arXiv 2020.  [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2003.00982.pdf).\n\n44. Dai Quoc Nguyen, Tu Dinh Nguyen, Dinh Phung. **Universal Self-Attention Network for Graph Classification**. arXiv 2020.  [paper](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1909.11855.pdf)\n\n## ![#f03c15](https:\u002F\u002Fvia.placeholder.com\u002F15\u002Ff03c15\u002F000000?text=+) `Open source platforms on GNN`:\n\n1. **Deep Graph Library（DGL)**\n\n  DGL is developed and maintained by New York University, New York University Shanghai, AWS Shanghai Research Institute and AWS MXNet     Science Team. \n\n  Initiation time: 2018.\n\n  Source: [URL](https:\u002F\u002Fwww.dgl.ai\u002F), [github](https:\u002F\u002Fgithub.com\u002Fjermainewang\u002Fdgl)\n\n2. **NGra**\n\n  NGra is developed and maintained by Peking University and Microsoft Asia Research Institute. \n\n  Initiation time:2018\n\n  Source: [pdf](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1810.08403.pdf)\n\n3. **Graph_nets**\n\n  Graph_nets is developed and maintained by [DeepMind](https:\u002F\u002Fdeepmind.com\u002F), Google Corp.\n\n  Initiation time:2018\n\n  Source: [github](https:\u002F\u002Fgithub.com\u002Fdeepmind\u002Fgraph_nets)\n  \n4. **Euler**\n\n  Euler is developed and maintained by Alimama, which belongs to Alibaba Group.\n\n  Initiation time:2019\n\n  Source: [github](https:\u002F\u002Fgithub.com\u002Falibaba\u002Feuler)\n  \n5. **PyTorch Geometric**\n  \n  PyTorch Geometric is developed and maintained by TU Dortmund University, Germany.\n  \n  Initiation time:2019\n  \n  Source: [github](https:\u002F\u002Fgithub.com\u002Frusty1s\u002Fpytorch_geometric) [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1903.02428?context=cs.LG)\n  \n6. **PyTorch-BigGraph（PBG）**\n\n  PBG is developed and maintained by Facebook AI Research.\n\n  Initiation time:2019\n\n  Source: [github](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002FPyTorch-BigGraph) [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1903.12287) \n  \n7. **Angel**\n\n  Angel is developed and maintained by Tencent Inc.\n  \n  Initiation time:2019\n  \n  Source: [github](https:\u002F\u002Fgithub.com\u002FAngel-ML\u002Fangel)\n  \n8. **Plato**\n\n  Plato is developed and maintained by Tencent Inc.\n  \n  Initiation time:2019\n  \n  Source: [github](https:\u002F\u002Fgithub.com\u002Ftencent\u002Fplato)\n  \n 9. **PGL**\n\n  PGL is developed and maintained by Baidu Inc.\n  \n  Initiation time:2019\n  \n  Source: [github](https:\u002F\u002Fgithub.com\u002FPaddlePaddle\u002FPGL)\n  \n10. **OGB**\n\n  Open Graph Benchmark(OGB) is developed and maintained by Standford University.\n   \n  Initiation time:2019\n  \n  Source: [github](http:\u002F\u002Fogb.stanford.edu)\n\n11. **Benchmarking GNNs**\n\n  Benchmarking GNNs is developed and maintained by Nanyang Technological University.\n   \n  Initiation time:2020\n  \n  Source: [github](https:\u002F\u002Fgithub.com\u002Fgraphdeeplearning\u002Fbenchmarking-gnns)\n\n12. **Graph-Learn**\n\n  Graph-Learn is developed and maintained by Alibaba Group.\n\n  Initiation time:2020\n\n  Source: [github](https:\u002F\u002Fgithub.com\u002Falibaba\u002Fgraph-learn)\n  \n13. **AutoGL (Auto Graph Learning)** **New**\n\n  AutoGL is developed and maintained by Tsinghua University.\n\n  Initiation time:2020\n\n  Source: [github](https:\u002F\u002Fgithub.com\u002FTHUMNLab\u002FAutoGL)\n\n\n\n## ![#f03c15](https:\u002F\u002Fvia.placeholder.com\u002F15\u002Ff03c15\u002F000000?text=+) `Appetizer：Art Exhibition of Network\u002FGraph Structured Data`:\n\n  ![image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjdlc105_Must-read-papers-and-continuous-tracking-on-Graph-Neural-Network-GNN-progress_readme_cc0b37d5a8a2.gif)\n \n 1. **The interesting Social Network**.\n\n  ![image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjdlc105_Must-read-papers-and-continuous-tracking-on-Graph-Neural-Network-GNN-progress_readme_1eef02605e3e.jpg)\n \n 2. **The beauty of the Biological Network**.\n\n\n\n\n\n\n\n","# 图神经网络（GNN）必读论文及进展持续跟踪\n\n许多重要的现实世界应用和问题都以图的形式呈现，例如社交网络、蛋白质-蛋白质相互作用网络、脑网络、化学分子图以及三维点云等。因此，在上述跨学科研究的推动下，针对图数据的神经网络模型已成为新兴的研究热点。***GNN及其变体是一类新兴且强大的神经网络模型。其应用已不再局限于最初的社交网络领域，而是在数据可视化、图像处理、自然语言处理、推荐系统、计算机视觉、生物信息学、化学信息学、药物研发等多个领域蓬勃发展。*** 本项目专注于GNN，列出相关必读论文并持续跟踪其进展。需要注意的是，GNN的实际整体进展应涵盖但不限于这些论文。我们期待推动这一方向的发展，并为该领域的研究人员提供一些帮助。\n\n由Allen Bluce（Bentian Li）和Anne Bluce（Yunxia Lin）共同贡献。如发现任何错误或与GNN相关的问题，欢迎发送邮件至：jdlc105@qq.com 或 lbtjackbluce@gmail.com。\n\n***技术关键词：图神经网络、图卷积网络、图网络、图注意力网络、图自编码器、图卷积强化学习、图胶囊神经网络……***\n\n```diff\n+ 极热的研究课题：\n```\n***最具代表性的作品——T.N. Kipf和M. Welling提出的基于图卷积网络（GCNs）的半监督分类方法（ICLR2017会议论文列表中的第5篇）在Google Scholar上已被引用1,020次（截至2019年5月9日）。*** 更新：1,065次（2019年5月20日）；更新：1,106次（2019年5月27日）；更新：1,227次（2019年6月19日）；更新：1,377次（2019年7月8日）；更新：1,678次（2019年9月17日）；更新：1,944次（2019年10月29日）；更新：2,232次（2019年12月9日）；更新：2,677次（2020年2月2日）。更新：3,018次（2020年3月17日）；更新：3,560次（2020年5月27日）；更新：4,060次（2020年7月3日）；更新：5,371次（2020年10月25日）。更新：6,258次（2021年1月1日）。更新：6,672次（2021年2月7日）。更新：8,454次（2021年6月16日）。更新：14,251次（2022年4月21日）。**更新：22,270次（2023年3月28日）**。\n\n***项目启动时间：2018年12月11日，最新更新时间：2023年3月28日***。感谢全球各地开发者和科学家在Github上给予我们的众多星标和支持！！！我们将继续努力使该项目更加完善。\n\n```diff\n+ 新闻：关于GNN模型及其应用的最新论文来自ICML2022、KDD2022等会议。我们正在等待更多论文的发布。\n```\n\n\n## ![#f03c15](https:\u002F\u002Fvia.placeholder.com\u002F15\u002Ff03c15\u002F000000?text=+) `综述论文`：\n\n1. 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Gusi Te, Wei Hu, Amin Zheng, Zongming Guo, **RGCNN：用于点云分割的正则化图卷积网络**。ACM多媒体2018。[论文](http:\u002F\u002Fwww.icst.pku.edu.cn\u002FF\u002Fintro\u002Fhuwei\u002Findex_files\u002Fpapers\u002FACMMM18_Te.pdf), [代码](https:\u002F\u002Fgithub.com\u002Ftegusi\u002FRGCNN),\n\n20. Talukdar, Partha, Shikhar Vashishth, Shib Sankar Dasgupta 和 Swayambhu Nath Ray。**利用图卷积网络对文献进行年代测定**。ACL 2018。[论文](http:\u002F\u002Fanthology.aclweb.org\u002Fattachments\u002FP\u002FP18\u002FP18-1149.Poster.pdf), [代码](http:\u002F\u002Fgithub.com\u002Fmalllabiisc\u002Fneuraldater)\n\n21. Sanchez-Gonzalez A , Heess N , Springenberg J T , 等。**图网络作为可学习的物理引擎用于推理和控制**。ICML 2018。[论文](http:\u002F\u002Fproceedings.mlr.press\u002Fv80\u002Fsanchez-gonzalez18a\u002Fsanchez-gonzalez18a.pdf)\n\n22. Muhan Zhang, Yixin Chen。**基于图神经网络的链接预测**。NeurIPS(NIPS) 2018。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1802.09691.pdf)\n\n23. Chen, Jie, Tengfei Ma, 和 Cao Xiao。**FastGCN：通过重要性采样实现的快速图卷积网络学习**。ICLR 2018。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1801.10247.pdf)\n\n24. Zhang, Zhen, Hongxia Yang, Jiajun Bu, Sheng Zhou, Pinggang Yu, Jianwei Zhang, Martin Ester, 和 Can Wang。**ANRL：基于深度神经网络的属性网络表示学习**。IJCAI 2018。[论文](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2018\u002F438)\n\n25. Rahimi A , Cohn T , Baldwin T 。**利用图卷积网络进行半监督用户地理位置定位**。ACL 2018。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1804.08049.pdf)\n\n26. Morris C , Ritzert M , Fey M , 等。**Weisfeiler和Leman走向神经网络：高阶图神经网络**。AAAI 2019。[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1810.02244v1)\n\n27. Xu K, Hu W, Leskovec J, 等。**图神经网络有多强大？**，ICLR 2019。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1810.00826.pdf)\n\n28. Johannes Klicpera, Aleksandar Bojchevski, Stephan Günnemann。**将神经网络与个性化PageRank结合用于图分类**，ICLR 2019。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1810.05997.pdf)\n\n29. Daniel Zügner, Stephan Günnemann。**基于元学习的图神经网络对抗攻击**，ICLR 2019。[论文](https:\u002F\u002Fopenreview.net\u002Fpdf?id=Bylnx209YX)\n\n30. Zhang Xinyi, Lihui Chen。**胶囊图神经网络**，ICLR 2019。[论文](https:\u002F\u002Fopenreview.net\u002Fpdf?id=Byl8BnRcYm)\n\n31. Liao, R., Zhao, Z., Urtasun, R., 和 Zemel, R。**LanczosNet：多尺度深度图卷积网络**，ICLR 2019，[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1901.01484)\n\n32. Bingbing Xu, Huawei Shen, Qi Cao, Yunqi Qiu, Xueqi Cheng。**图小波神经网络**，ICLR 2019，[论文](https:\u002F\u002Fopenreview.net\u002Fforum?id=H1ewdiR5tQ)\n\n33. Hu J, Guo C, Yang B, 等。**利用图卷积网络对道路网络进行随机权重补全** ICDE。2019。[论文](https:\u002F\u002Fopenreview.net\u002Fforum?id=H1ewdiR5tQ)\n\n34. Yao L, Mao C, Luo Y 。**用于文本分类的图卷积网络**。AAAI 2019。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1809.05679.pdf)\n\n35. Landrieu L , Boussaha M 。**基于图结构的深度度量学习的点云超分割**。CVPR 2019。[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1904.02113)\n\n36. Si C , Chen W , Wang W , 等。**一种用于基于骨骼的动作识别的注意力增强型图卷积LSTM网络**。CVPR 2019。[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1902.09130)\n\n37. Cucurull G , Taslakian P , Vazquez D 。**上下文感知的视觉兼容性预测**。CVPR 2019。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1902.03646.pdf)\n\n38. Jia-Xing Zhong, Nannan Li, Weijie Kong, Shan Liu, Thomas H. Li, Ge Li。**图卷积标签噪声清理器：训练即插即用的动作分类器用于异常检测**。CVPR 2019。[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1903.07256)\n\n39. Michael Kampffmeyer, Yinbo Chen, Xiaodan Liang, Hao Wang, Yujia Zhang, Eric P. Xing。**重新思考知识图谱传播用于零样本学习**。CVPR 2019。[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.11724v3)\n\n40. Arushi Goel, Keng Teck Ma, Cheston Tan。**用于生成社交关系图的端到端网络**。CVPR 2019。[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1903.09784)\n\n41. Yichao Yan, Qiang Zhang, Bingbing Ni, Wendong Zhang, Minghao Xu, Xiaokang Yang。**用于人员搜索的上下文图学习**。CVPR 2019 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1904.01830.pdf)\n\n42. Zhongdao Wang, Liang Zheng, Yali Li, Shengjin Wang。**基于图卷积网络的人脸聚类**。CVPR 2019 [论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1903.11306)\n\n43. Lei Yang, Xiaohang Zhan, Dapeng Chen, Junjie Yan, Chen Change Loy, Dahua Lin。**在亲和力图上学习人脸聚类**。CVPR 2019 [论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1904.02749.pdf)\n\n44. Yao Ma, Suhang Wang, Charu C. Aggarwal, Jiliang Tang。**带有特征池化的图卷积网络**。KDD2019，[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1904.13107.pdf)\n\n45. Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, Dawei Yin。**用于社交推荐的图神经网络**。WWW2019，[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1902.07243.pdf)\n\n46. Kim J, Kim T, Kim S, 等。**用于少样本学习的边标注图神经网络**。CVPR 2019。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.01436.pdf)\n\n47. Jessica V. Schrouff, Kai Wohlfahrt, Bruno Marnette, Liam Atkinson。**利用图神经网络推断JavaScript类型**。ICLR 2019。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.06707.pdf)\n\n48. Emanuele Rossi, Federico Monti, Michael Bronstein, Pietro liò。**使用图卷积网络进行ncRNA分类**。SIGKDD 2019。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.06515.pdf)\n\n49. Wu F, Zhang T, Souza Jr A H, 等。**简化图卷积网络**。ICML 2019。[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1902.07153).\n\n50. Junhyun Lee, Inyeop Lee, Jaewoo Kang。**自注意力图池化**。ICML 2019。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1904.08082.pdf)。\n\n51. Chiang W L, Liu X, Si S, 等。**Cluster-GCN：一种高效训练深度大型图卷积网络的算法**。SIGKDD 2019。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.07953.pdf)。\n\n52. Namyong Park, Andrey Kan, Xin Luna Dong, Tong Zhao, Christos Faloutsos。**利用图神经网络估计知识图谱中节点的重要性**。SIGKDD 2019。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.08865.pdf)。\n\n53. Wu S, Tang Y, Zhu Y, 等。**基于会话的推荐与图神经网络**。AAAI 2019。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1811.00855.pdf)。\n\n54. Qu M, Bengio Y, Tang J。**GMNN：图马尔可夫神经网络**。ICML 2019。[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1905.06214)[代码](https:\u002F\u002Fgithub.com\u002FDeepGraphLearning\u002FGMNN)。\n\n55. Li Y, Gu C, Dullien T, 等。**图匹配网络用于学习图结构对象的相似性**，ICML 2019。[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1904.12787)。\n\n56. Gao H, Ji S。**图U-Nets**，ICML 2019。[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1905.05178)。\n\n57. Bojchevski A, Günnemann S。**通过图中毒对节点嵌入进行对抗攻击**，ICML 2019。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1809.01093.pdf)。\n\n58. Jeong D, Kwon T, Kim Y 等。**用于乐谱数据的图神经网络及表现性钢琴演奏建模**。ICML 2019。[论文](http:\u002F\u002Fproceedings.mlr.press\u002Fv97\u002Fjeong19a\u002Fjeong19a.pdf)。\n\n59. Zhang G, He H, Katabi D。**Circuit-GNN：面向分布式电路设计的图神经网络**。ICML 2019。[论文](http:\u002F\u002Fproceedings.mlr.press\u002Fv97\u002Fzhang19e\u002Fzhang19e.pdf)。\n\n60. Alet F, Jeewajee A K, Bauza M 等。**图元网络：自适应、结构化的计算与记忆**，ICML 2019。[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1904.09019)。\n\n61. Rieck B, Bock C, Borgwardt K。**用于图分类的持久化 Weisfeiler-Lehman 过程**，ICML 2019。[论文](http:\u002F\u002Fbastian.rieck.me\u002Fresearch\u002FICML2019_P-WL.pdf)。\n\n62. Walker I, Glocker B。**图卷积高斯过程**，ICML 2019。[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1905.05739)。\n\n63. Yu Y, Chen J, Gao T 等。**DAG-GNN：基于图神经网络的 DAG 结构学习**，ICML 2019。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1904.10098.pdf)。\n\n64. Guo Zhijiang、Zhang Yan 和 Lu Wei。**用于关系抽取的注意力引导图卷积网络** ACL 2019。[论文](http:\u002F\u002Fwww.statnlp.org\u002Fpaper\u002F2019\u002Fattention-guided-graph-convolutional-networks-relation-extraction.html)。[代码](https:\u002F\u002Fgithub.com\u002FCartus\u002FAGGCN_TACRED)。\n\n65. Li Chang、Goldwasser Dan。**利用图卷积网络编码社交信息以检测新闻媒体中的政治立场** ACL 2019。[论文](https:\u002F\u002Fwww.cs.purdue.edu\u002Fhomes\u002Fdgoldwas\u002F\u002Fdownloads\u002Fpapers\u002FLiG_acl_2019.pdf)。\n\n66. Zhu Hao、Lin Yankai、Liu Zhiyuan、Fu Jie、Chua Tat-seng、Sun Maosong。**用于关系抽取的参数生成式图神经网络** ACL 2019。[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1902.00756)。\n\n67. Vashishth Shikhar、Bhandari Manik、Yadav Prateek、Rai Piyush、Bhattacharyya Chiranjib、Talukdar Partha。**利用图卷积网络在词嵌入中融入句法和语义信息** ACL 2019。[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.04283)。\n\n68. Cui Z、Li Z、Wu S 等。**整体穿搭：基于节点级图神经网络的服装搭配学习** WWW 2019。[论文](https:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?doid=3308558.3313444)。\n\n69. Zhang, Chris 等。**用于神经架构搜索的图超网络**。ICLR 2019。[论文](https:\u002F\u002Fopenreview.net\u002Fpdf?id=rkgW0oA9FX)。\n\n70. Chen, Zhengdao 等。**基于线图神经网络的监督式社区发现**。ICLR 2019。[论文](https:\u002F\u002Fopenreview.net\u002Fpdf?id=H1g0Z3A9Fm)。\n\n71. Maron, Haggai 等。**不变与等变图网络**。ICLR 2019。[论文](https:\u002F\u002Fopenreview.net\u002Fpdf?id=Syx72jC9tm)。\n\n72. Gulcehre, Caglar 等。**双曲注意力网络**。ICLR 2019。[论文](https:\u002F\u002Fopenreview.net\u002Fpdf?id=rJxHsjRqFQ)。\n\n73. Prates, Marcelo O. R. 等。**学习求解 NP 完全问题——用于决策型 TSP 的图神经网络**。AAAI 2019。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1809.02721.pdf)。\n\n74. Liu, Ziqi 等。**GeniePath：具有自适应感受野路径的图神经网络**。AAAI 2019。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1802.00910.pdf)。\n\n75. Keriven N、Peyré G。**通用的不变与等变图神经网络**。NeurIPS 2019。[论文](https:\u002F\u002Farxiv.gg363.site\u002Fabs\u002F1905.04943)。\n\n76. Liu Qi 等。**双曲图神经网络**。NeurIPS 2019。[论文](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F9033-hyperbolic-graph-neural-networks.pdf)。\n\n77. Ying Zhitao 等。**GNNExplainer：为图神经网络生成解释**。NeurIPS 2019。[论文](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F9123-gnnexplainer-generating-explanations-for-graph-neural-networks.pdf)。\n\n78. Zhou Yaqin 等。**Devign：通过图神经网络学习全面的程序语义以有效识别漏洞**。NeurIPS 2019。[论文](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F9209-devign-effective-vulnerability-identification-by-learning-comprehensive-program-semantics-via-graph-neural-networks.pdf)。\n\n79. Hajiramezanali Ehsan 等。**变分图递归神经网络**。NeurIPS 2019。[论文](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F9254-variational-graph-recurrent-neural-networks.pdf)。\n\n80. Luan Sitao 等。**突破天花板：更强大的多尺度深度图卷积网络**。NeurIPS 2019。[论文](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F9276-break-the-ceiling-stronger-multi-scale-deep-graph-convolutional-networks.pdf)。\n\n81. Zou Difan 等。**用于训练深度且大型图卷积网络的层依赖重要性采样**。NeurIPS 2019。[论文](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F9303-layer-dependent-importance-sampling-for-training-deep-and-large-graph-convolutional-networks.pdf)。\n\n82. Yun Seongjun 等。**图变换器网络**。NeurIPS 2019。[论文](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F9367-graph-transformer-networks.pdf)。\n\n83. Nicolicioiu Andrei 等。**循环时空图神经网络**。NeurIPS 2019。[论文](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F9444-recurrent-space-time-graph-neural-networks.pdf)。\n\n84. Dehmamy Nima 等。**理解图神经网络在学习图拓扑结构方面的表示能力**。NeurIPS 2019。[论文](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F9675-understanding-the-representation-power-of-graph-neural-networks-in-learning-graph-topology.pdf)。\n\n85. Gasse Maxime 等。**利用图卷积神经网络进行精确组合优化**。NeurIPS 2019。[论文](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F9690-exact-combinatorial-optimization-with-graph-convolutional-neural-networks.pdf)。\n\n86. Chen Zhengdao 等。**关于图同构测试与使用 GNN 进行函数逼近之间等价性的研究**。NeurIPS 2019。[论文](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F9718-on-the-equivalence-between-graph-isomorphism-testing-and-function-approximation-with-gnns.pdf)。\n\n87. Kosaraju Vineet 等。**Social-BiGAT：利用自行车 GAN 和图注意力网络进行多模态轨迹预测**。NeurIPS 2019。[论文](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F8308-social-bigat-multimodal-trajectory-forecasting-using-bicycle-gan-and-graph-attention-networks.pdf)。\n\n88. Yang Carl 等。**通过图变分生成对抗网络进行条件结构生成**。NeurIPS 2019。[论文](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F8415-conditional-structure-generation-through-graph-variational-generative-adversarial-nets.pdf)。\n\n89. Yadati Naganand 等。**HyperGCN：一种在超图上训练图卷积网络的新方法**。NeurIPS 2019。[论文](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F8430-hypergcn-a-new-method-for-training-graph-convolutional-networks-on-hypergraphs.pdf)。\n\n90. Maron Haggai 等。**可证明功能强大的图网络**。NeurIPS 2019。[论文](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F8488-provably-powerful-graph-networks.pdf)。\n\n91. Nachmani Eliya 等。**用于分组码的超图网络解码器**。NeurIPS 2019。[论文](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F8504-hyper-graph-network-decoders-for-block-codes.pdf)。\n\n92. 戴汉军等。**学习可迁移的图探索**。NeurIPS，2019年。[论文](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F8521-learning-transferable-graph-exploration.pdf)。\n\n93. 佐藤龙马等。**图神经网络在组合优化问题中的近似比**。NeurIPS，2019年。[论文](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F8662-approximation-ratios-of-graph-neural-networks-for-combinatorial-problems.pdf)。\n\n94. 鲍里斯·克尼亚泽夫等。**理解图神经网络中的注意力机制与泛化能力**。NeurIPS，2019年。[论文](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F8673-understanding-attention-and-generalization-in-graph-neural-networks.pdf)。\n\n95. 廖仁杰等。**利用图递归注意力网络高效生成图结构**。NeurIPS，2019年。[论文](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F8678-efficient-graph-generation-with-graph-recurrent-attention-networks.pdf)。\n\n96. 布莱恩·怀尔德等。**图上的端到端学习与优化**。NeurIPS，2019年。[论文](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F8715-end-to-end-learning-and-optimization-on-graphs.pdf)。\n\n97. 西蒙·杜等。**图神经切空间核：将图神经网络与图核融合**。NeurIPS，2019年。[论文](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F8809-graph-neural-tangent-kernel-fusing-graph-neural-networks-with-graph-kernels.pdf)。\n\n98. W. O. K. Asiri Suranga Wijesinghe等。**DFNets：用于具有反馈环滤波器的图的谱卷积神经网络**。NeurIPS，2019年。[论文](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F8834-dfnets-spectral-cnns-for-graphs-with-feedback-looped-filters.pdf)。\n\n99. 舒东旭等。**基于树状图卷积的3D点云生成对抗网络**。ICCV 2019。[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1905.06292)\n\n100. 蔡宇俊等。**利用图卷积网络挖掘时空关系进行3D姿态估计**。ICCV 2019。[论文](https:\u002F\u002Fcse.buffalo.edu\u002F~jsyuan\u002Fpapers\u002F2019\u002FExploiting_Spatial-temporal_Relationships_for_3D_Pose_Estimation_via_Graph_Convolutional_Networks.pdf)\n\n101. 曾润豪等。**用于时序动作定位的图卷积网络**。ICCV 2019。[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1909.03252)\n\n102. 毕音等。**面向神经形态视觉感知的基于图的对象分类**。ICCV 2019。[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1908.06648)\n\n103. 陈天水等。**为多标签图像识别学习语义特定的图表示**。ICCV 2019。[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1908.07325)\n\n104. 李林洁等。**用于视觉问答的关系感知图注意力网络**。ICCV 2019。[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1903.12314)\n\n105. 朴智雄等。**对称图卷积自编码器用于无监督图表示学习**。ICCV 2019。[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1908.02441)\n\n106. 王润中等。**用于深度图匹配的组合嵌入网络学习**。ICCV 2019。[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1904.00597)\n\n107. 陶志强等。**用于集成聚类的对抗性图嵌入**。IJCAI 2019。[论文](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2019\u002F0494.pdf)\n\n108. 张晓彤等。**通过自适应图卷积进行属性图聚类**。IJCAI 2019。[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1906.01210)\n\n109. 蒋建文等。**动态超图神经网络**。IJCAI 2019。[论文](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2019\u002F0366.pdf)\n\n110. 朴浩根等。**利用交互边进行节点分类的深度图神经网络**。IJCAI 2019。[论文](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2019\u002F0447.pdf)\n\n111. 彭浩等。**利用异质图卷积网络进行细粒度事件分类**。IJCAI 2019。[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1906.04580)\n\n112. 徐成峰等。**用于会话式推荐的图上下文自注意力网络**。IJCAI 2019。[论文](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2019\u002F0547.pdf)\n\n113. 徐瑞青等。**用于跨模态检索的图卷积网络哈希**。IJCAI 2019。[论文](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2019\u002F0138.pdf)\n\n114. 许冰冰等。**使用热核的图卷积网络用于半监督学习**。IJCAI 2019。[论文](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2019\u002F0267.pdf)\n\n115. 吴宗翰等。**用于深度时空图建模的图WaveNet**。IJCAI 2019。[论文](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2019\u002F0264.pdf)\n\n116. 胡奋宇等。**用于半监督节点分类的层次化图卷积网络**。IJCAI 2019。[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1902.06667)\n\n117. 郑莉等。**AddGraph：基于注意力的时序GCN用于动态图中的异常检测**。IJCAI 2019。[论文](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2019\u002F0614.pdf)\n\n118. 杨亮等。**双自步图卷积网络：旨在减少由拓扑结构引起的属性扭曲**。IJCAI 2019。[论文](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2019\u002F0564.pdf)\n\n119. 杨亮等。**掩码图卷积网络**。IJCAI 2019。[论文](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2019\u002F0565.pdf)\n\n120. 徐晓峰等。**通过双曲邻域图传播学习图像特定属性**。IJCAI 2019。[论文](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2019\u002F0554.pdf)\n\n121. Li G、Müller M、Thabet A等。**GCN能否像CNN一样深？**。ICCV 2019。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1904.03751.pdf)。\n\n122. 朴C、李C、方H等。**STGRAT：用于交通预测的时空图注意力网络**。AAAI 2020。[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1911.13181)。\n\n123. 刘Y、王X、吴S等。**促进独立性的图解耦网络**。AAAI 2020。[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1911.11430)。\n\n123. 史H、范H、郭J T。**利用三元闭包实现图自编码器的有效解码**。AAAI 2020。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1911.11322.pdf)。\n\n124. 王X、王R、史C等。**多组件图卷积协同过滤**。AAAI 2020。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1911.10699.pdf)。\n\n125. 苏J、贝灵P A、郭R等。**用于驾驶加速度概率建模的图卷积网络**。AAAI 2020。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1911.09837.pdf)。\n\n126. 克劳迪奥·加利奇奥和阿莱西奥·米凯利。**快速且深层的图神经网络**。AAAI 2020。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1911.08941.pdf)。\n\n127. 彭W、洪X、陈H等。**通过神经搜索学习用于基于骨架的人体动作识别的图卷积网络**。AAAI 2020。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1911.04131.pdf)。\n\n128. 帕利瓦尔A、卢斯S、拉贝M等。**用于高阶逻辑和定理证明的图表示**。AAAI 2020。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1905.10006.pdf)。\n\n129. 大野健太等。**图神经网络在节点分类任务中会指数级丧失表达能力**。ICLR 2020。[论文](https:\u002F\u002Fopenreview.net\u002Fpdf?id=S1ldO2EFPr)。\n\n130. 张木涵，等。**基于图神经网络的归纳矩阵补全**。ICLR 2020。[论文](https:\u002F\u002Fopenreview.net\u002Fpdf?id=ByxxgCEYDS)。\n\n131. 巴勃罗·巴塞洛，等。**图神经网络的逻辑表达能力**。ICLR 2020。[论文](https:\u002F\u002Fopenreview.net\u002Fpdf?id=r1lZ7AEKvB)\n\n132. 胡伟华，等。**图神经网络的预训练策略**。ICLR 2020。[论文](https:\u002F\u002Fopenreview.net\u002Fpdf?id=HJlWWJSFDH)\n\n133. 佩洪斌，等。**Geom-GCN：几何图卷积网络**。ICLR 2020。[论文](https:\u002F\u002Fopenreview.net\u002Fpdf?id=S1e2agrFvS)\n\n134. 叶泽，等。**曲率图网络**。ICLR 2020。[论文](https:\u002F\u002Fopenreview.net\u002Fpdf?id=BylEqnVFDB)\n\n135. 安德烈亚斯·卢卡斯，等。**图神经网络学不到什么：深度与宽度的对比**。ICLR 2020。[论文](https:\u002F\u002Fopenreview.net\u002Fforum?id=B1l2bp4YwS)\n\n136. 费德里科·埃里卡，等。**图分类任务中图神经网络的公平比较**。ICLR 2020。[论文](https:\u002F\u002Fopenreview.net\u002Fpdf?id=HygDF6NFPB)\n\n137. 张凯，等。**用于图注意力网络的自适应结构指纹**。ICLR 2020。[论文](https:\u002F\u002Fopenreview.net\u002Fpdf?id=BJxWx0NYPr)\n\n138. 希卡尔·瓦希什特，等。**基于组合的多关系图卷积网络**。ICLR 2020。[论文](https:\u002F\u002Fopenreview.net\u002Fpdf?id=BylA_C4tPr)\n\n139. 魏嘉怡，等。**LambdaNet：利用图神经网络进行概率类型推断**。ICLR 2020。[论文](https:\u002F\u002Fopenreview.net\u002Fpdf?id=Hkx6hANtwH)\n\n140. 江杰川，等。**图卷积强化学习**。ICLR 2020。[论文](https:\u002F\u002Fopenreview.net\u002Fpdf?id=HkxdQkSYDB)\n\n141. 侯一凡，等。**衡量并改进图神经网络中图信息的使用**。ICLR 2020。[论文](https:\u002F\u002Fopenreview.net\u002Fpdf?id=rkeIIkHKvS)\n\n142. 张若驰，等。**Hyper-SAGNN：一种基于自注意力机制的超图神经网络**。ICLR 2020。[论文](https:\u002F\u002Fopenreview.net\u002Fpdf?id=ryeHuJBtPH)\n\n143. 荣宇，等。**DropEdge：迈向节点分类任务中的深层图卷积网络**。ICLR 2020。[论文](https:\u002F\u002Fopenreview.net\u002Fpdf?id=Hkx1qkrKPr)\n\n144. 张雨雨，等。**利用图神经网络实现高效的概率逻辑推理**。ICLR 2020。[论文](https:\u002F\u002Fopenreview.net\u002Fpdf?id=rJg76kStwH)\n\n145. 阿米尔·侯赛因·卡萨哈马迪，等。**基于记忆的图网络**。ICLR 2020。[论文](https:\u002F\u002Fopenreview.net\u002Fpdf?id=r1laNeBYPB)\n\n146. 韩青曾，等。**GraphSAINT：基于图采样的归纳学习方法**。ICLR 2020。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1907.04931.pdf)\n\n147. 林江科，等。**利用图卷积网络从野外图像中重建高保真度三维人脸**。CVPR 2020。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2003.05653.pdf)\n\n148. 奥伊通·乌卢坦，等。**VSGNet：基于图卷积的空间注意力网络，用于检测人体与物体的交互**。CVPR 2020。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2003.05541.pdf)\n\n149. 徐强根，等。**Grid-GCN：用于快速且可扩展点云学习的网格图卷积网络**。CVPR 2020。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1912.02984.pdf)\n\n150. 阿卜杜拉·穆罕默德和钱坤，**Social-STGCNN：用于人类轨迹预测的社会时空图卷积神经网络**。CVPR 2020。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2002.11927.pdf)\n\n151. 张凯华，等。**具有注意力图聚类的自适应图卷积网络，用于共同显著性检测**。CVPR 2020。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2003.06167.pdf)\n\n152. 沈佳明，等。**TaxoExpan：基于位置增强的图神经网络的自监督分类体系扩展**。WWW 2020。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2001.09522.pdf)\n\n153. 博德宇，等。**结构化深度聚类网络**。WWW 2020。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2002.01633.pdf)\n\n154. 付新宇，等。**MAGNN：用于异质图嵌入的元路径聚合图神经网络**。WWW 2020。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2002.01680.pdf)\n\n155. 吴曼，等。**无监督域适应图卷积网络**。WWW 2020。[论文](https:\u002F\u002Fwww.researchgate.net\u002Fpublication\u002F338844424_Unsupervised_Domain_Adaptive_Graph_Convolutional_Networks)\n\n156. 孙毅伟，等。**通过节点注入对图神经网络进行对抗攻击：一种分层强化学习方法**。WWW 2020。[论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fpdf\u002F10.1145\u002F3366423.3380149)\n\n157. 王晓阳，等。**基于时空图神经网络的交通流量预测**。WWW 2020。[论文](https:\u002F\u002Fdoi.org\u002F10.1145\u002F3366423.3380186)\n\n158. 谭巧玉，等。**利用图神经网络为推荐系统学习哈希函数**。WWW 2020。[论文](https:\u002F\u002Fdoi.org\u002F10.1145\u002F3366423.3380266)\n\n159. 曲亮，等。**基于时序依赖图神经网络的连续时间链接预测**。WWW 2020。[论文](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fpdf\u002F10.1145\u002F3366423.3380073)\n\n160. 金伟，等。**用于鲁棒图神经网络的图结构学习**。KDD 2020。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2005.10203.pdf)，[代码](https:\u002F\u002Fgithub.com\u002FChandlerBang\u002FPro-GNN)。\n\n161. 武宗翰，等。**连点成线：利用图神经网络进行多变量时间序列预测**。KDD 2020。[论文](https:\u002F\u002Fshiruipan.github.io\u002Fpublication\u002Fkdd-2020-wu\u002Fkdd-2020-wu.pdf)。\n\n162. 杨震，等。**理解图表示学习中的负采样**。KDD 2020。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2005.09863.pdf)。\n\n163. 王孟涵，等。**M2GRL：面向大规模推荐系统的多任务多视图图表示学习框架**。KDD 2020。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2005.10110.pdf)。\n\n164. 路易斯-帕斯卡尔 A. C. 克索内克斯，等。**连续图神经网络**。ICML 2020。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1912.00967.pdf)。\n\n165. 马克·布罗克施密特，等。**GNN-FiLM：带有特征逐元素线性调制的图神经网络**。ICML 2020。[论文](https:\u002F\u002Fproceedings.mlr.press\u002Fv119\u002Fbrockschmidt20a.html)。\n\n166. 阿尔曼·哈桑扎德，等。**具有自适应连接采样的贝叶斯图神经网络**。ICML 2020。[论文](https:\u002F\u002Fproceedings.mlr.press\u002Fv119\u002Fhasanzadeh20a.html)。\n\n167. 菲利佩·德·阿维拉·贝尔布特-佩雷斯，等。**将可微分偏微分方程求解器与图神经网络结合用于流体流动预测**。ICML 2020。[论文](https:\u002F\u002Fproceedings.mlr.press\u002Fv119\u002Fde-avila-belbute-peres20a.html)。\n\n168. 伊拉伊·卢兹，等。**利用图神经网络学习代数多重网格法**。ICML 2020。[论文](http:\u002F\u002Fproceedings.mlr.press\u002Fv119\u002Fluz20a.html)。\n\n169. 维卡斯·K·加格，等。**图神经网络的泛化能力和表示限制**。ICML 2020。[论文](http:\u002F\u002Fproceedings.mlr.press\u002Fv119\u002Fgarg20c.html)。\n\n170. 张帅，等。**保证泛化能力的图神经网络快速学习：单隐层情况**。ICML 2020。[论文](http:\u002F\u002Fproceedings.mlr.press\u002Fv119\u002Fzhang20y.html)。\n\n171. 菲利波，等。**利用图神经网络进行谱聚类以实现图池化**。ICML 2020。[论文](https:\u002F\u002Fproceedings.mlr.press\u002Fv119\u002Fbianchi20a.html)。\n\n172. 陈明等。**简单而深层的图卷积网络**。ICML 2020。[论文](https:\u002F\u002Fproceedings.mlr.press\u002Fv119\u002Fchen20v.html)。\n\n173. 游宇宁等。**自监督学习何时有助于图卷积网络？**。ICML 2020。[论文](http:\u002F\u002Fproceedings.mlr.press\u002Fv119\u002Fyou20a.html)。\n\n174. 格雷戈尔·巴赫曼等。**常曲率图卷积网络**。ICML 2020。[论文](http:\u002F\u002Fproceedings.mlr.press\u002Fv119\u002Fbachmann20a.html)。\n\n175. 于文辉等。**基于低通协同过滤的图卷积网络推荐系统**。ICML 2020。[论文](http:\u002F\u002Fproceedings.mlr.press\u002Fv119\u002Fyu20e.html)。\n\n176. 朱宏敏等。**具有邻居交互的双线性图神经网络**。IJCAI 2020。[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2002.03575)。\n\n177. 张硕等。**通过保持基数不变改进图神经网络中的注意力机制**。IJCAI 2020。[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1907.02204)。\n\n178. 周凯雄等。**多通道图神经网络**。IJCAI 2020。[论文](https:\u002F\u002Fwww.ijcai.org\u002FProceedings\u002F2020\u002F188)。\n\n179. 乔治·达索拉斯等。**用于节点消歧的着色图神经网络**。IJCAI 2020。[论文](https:\u002F\u002Fdoi.org\u002F10.24963\u002Fijcai.2020\u002F294)。\n\n180. 林轩等。**KGNN：用于药物—药物相互作用预测的知识图神经网络**。IJCAI 2020。[论文](https:\u002F\u002Fwww.ijcai.org\u002FProceedings\u002F2020\u002F380)。\n\n181. 庄元等。**利用图神经网络检测智能合约漏洞**。IJCAI 2020。[论文](https:\u002F\u002Fwww.ijcai.org\u002FProceedings\u002F2020\u002F454)。\n\n182. 贾子宇等。**GraphSleepNet：用于睡眠阶段分类的自适应时空图卷积网络**。IJCAI 2020。[论文](https:\u002F\u002Fwww.ijcai.org\u002FProceedings\u002F2020\u002F184)。\n\n183. 黄志超等。**MR-GCN：基于广义张量积的多关系图卷积网络**。IJCAI 2020。[论文](https:\u002F\u002Fwww.ijcai.org\u002FProceedings\u002F2020\u002F175)。\n\n184. 黄荣洲等。**LSGCN：基于图卷积网络的长短期交通流量预测**。IJCAI 2020。[论文](https:\u002F\u002Fwww.ijcai.org\u002FProceedings\u002F2020\u002F326)。\n\n185. 史敏等。**多分类不平衡数据下的图卷积网络学习**。IJCAI 2020。[论文](https:\u002F\u002Fwww.ijcai.org\u002FProceedings\u002F2020\u002F398)。\n\n186. 何东晓等。**面向无监督社区发现的以社区为中心的图卷积网络**。IJCAI 2020。[论文](https:\u002F\u002Fwww.ijcai.org\u002FProceedings\u002F2020\u002F486)。\n\n187. 卢安娜·鲁伊斯等。**图论神经网络与图神经网络的可迁移性**。NeurIPS 2020。[论文](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002F12bcd658ef0a540cabc36cdf2b1046fd-Abstract.html)。\n\n188. 迭戈·梅斯基塔等。**重新思考图神经网络中的池化操作**。NeurIPS 2020。[论文](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002F1764183ef03fc7324eb58c3842bd9a57-Abstract.html)。\n\n189. 彼得·韦利奇科维奇等。**指针图网络**。NeurIPS 2020。[论文](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002F176bf6219855a6eb1f3a30903e34b6fb-Abstract.html)。\n\n190. 安德烈亚斯·卢卡斯。**用图神经网络区分图结构到底有多难？**。NeurIPS 2020。[论文](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002F23685a2431acad7789c1e3d43ea1522c-Abstract.html)。\n\n191. 周尚辰等。**用于图像超分辨率的跨尺度内部图神经网络**。NeurIPS 2020。[论文](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002F23ad3e314e2a2b43b4c720507cec0723-Abstract.html)。\n\n192. 马佳琪等。**迈向更实用的图神经网络对抗攻击**。NeurIPS 2020。[论文](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002F32bb90e8976aab5298d5da10fe66f21d-Abstract.html)。\n\n193. 周凯雄等。**借助可微分组归一化构建更深的图神经网络**。NeurIPS 2020。[论文](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002F33dd6dba1d56e826aac1cbf23cdcca87-Abstract.html)。\n\n194. 本杰明·桑切斯-伦格林等。**评估图神经网络的特征重要性解释方法**。NeurIPS 2020。[论文](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002F417fbbf2e9d5a28a855a11894b2e795a-Abstract.html)。\n\n195. 刘子琪等。**用于训练图神经网络的赌博采样器**。NeurIPS 2020。[论文](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002F4cea2358d3cc5f8cd32397ca9bc51b94-Abstract.html)。\n\n196. 朱炯等。**超越图神经网络中的同质性：当前局限与有效设计**。NeurIPS 2020。[论文](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002F58ae23d878a47004366189884c2f8440-Abstract.html)。\n\n197. 艾米丽·阿尔森策等。**子图神经网络**。NeurIPS 2020。[论文](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002F5bca8566db79f3788be9efd96c9ed70d-Abstract.html)。\n\n198. 张震等。**因子图神经网络**。NeurIPS 2020。[论文](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002F61c66a2f4e6e10dc9c16ddf9d19745d6-Abstract.html)。\n\n199. 张翔等。**GNNGuard：防御图神经网络免受对抗攻击**。NeurIPS 2020。[论文](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002F690d83983a63aa1818423fd6edd3bfdb-Abstract.html)。\n\n200. 陈正道等。**图神经网络能否计数子结构？**。NeurIPS 2020。[论文](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002F75877cb75154206c4e65e76b88a12712-Abstract.html)。\n\n201. 顾方达等。**隐式图神经网络**。NeurIPS 2020。[论文](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002F8b5c8441a8ff8e151b191c53c1842a38-Abstract.html)。\n\n202. 武明等。**PGM-Explainer：图神经网络的概率图模型解释工具**。NeurIPS 2020。[论文](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002F8fb134f258b1f7865a6ab2d935a897c9-Abstract.html)。\n\n203. 西蒙·盖斯勒等。**通过鲁棒聚合实现可靠的图神经网络**。NeurIPS 2020。[论文](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002F99e314b1b43706773153e7ef375fc68c-Abstract.html)。\n\n204. 克莱芒·维尼亚克等。**利用结构化消息传递构建强大且等变的图神经网络**。NeurIPS 2020。[论文](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002Fa32d7eeaae19821fd9ce317f3ce952a7-Abstract.html)。\n\n205. 陈明等。**通过双向传播实现可扩展的图神经网络**。NeurIPS 2020。[论文](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002Fa7789ef88d599b8df86bbee632b2994d-Abstract.html)。\n\n206. 尼古伦佐斯·扬尼斯等。**随机游走图神经网络**。NeurIPS 2020。[论文](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002Fba95d78a7c942571185308775a97a3a0-Abstract.html)。\n\n207. 马郑等。**基于路径积分的图神经网络卷积与池化操作**。NeurIPS 2020。[论文](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002Fbe53d253d6bc3258a8160556dda3e9b2-Abstract.html)。\n\n208. 游家轩等。**图神经网络的设计空间**。NeurIPS 2020。[论文](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002Fc5c3d4fe6b2cc463c7d7ecba17cc9de7-Abstract.html)。\n\n209. 曹德福等。**用于多变量时间序列预测的谱时序图神经网络**。NeurIPS 2020。[论文](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002Fcdf6581cb7aca4b7e19ef136c6e601a5-Abstract.html)\n\n210. 大野健太等。**通过梯度提升进行转导的优化与泛化分析及其在多尺度图神经网络中的应用**。NeurIPS 2020。[论文](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002Fdab49080d80c724aad5ebf158d63df41-Abstract.html)\n\n211. 陈宇等。**面向图神经网络的迭代深度图学习：更优且鲁棒的节点嵌入**。NeurIPS 2020。[论文](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002Fe05c7ba4e087beea9410929698dc41a6-Abstract.html)\n\n212. 罗东升等。**图神经网络的参数化解释器**。NeurIPS 2020。[论文](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002Fe37b08dd3015330dcbb5d6663667b8b8-Abstract.html)\n\n213. 马丁·克利萨罗夫等。**利用图卷积网络进行奖励传播**。NeurIPS 2020。[论文](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002F970627414218ccff3497cb7a784288f5-Abstract.html)\n\n214. 民益孟等。**散射GCN：克服图卷积网络中的过度平滑问题**。NeurIPS 2020。[论文](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002Fa6b964c0bb675116a15ef1325b01ff45-Abstract.html)\n\n215. 白磊等。**用于交通预测的自适应图卷积递归网络**。NeurIPS 2020。[论文](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002Fce1aad92b939420fc17005e5461e6f48-Abstract.html)\n\n216. 莫舍·埃利亚索夫等。**DiffGCN：基于微分算子和代数多重网格池化的图卷积网络**。NeurIPS 2020。[论文](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002Fd16a974d4d6d0d71b29bfbfe045f1da7-Abstract.html)\n\n217. 潘特利斯·埃利纳斯等。**在缺乏图数据及对抗性场景下的图卷积网络变分推断**。NeurIPS 2020。[论文](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002Fd882050bb9eeba930974f596931be527-Abstract.html)\n\n218. 杨一丁等。**可分解的图卷积网络**。NeurIPS 2020。[论文](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002Fea3502c3594588f0e9d5142f99c66627-Abstract.html)\n\n219. 尼古拉斯·凯里万等。**大型随机图上图卷积网络的收敛性与稳定性**。NeurIPS 2020。[论文](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002Ff5a14d4963acf488e3a24780a84ac96c-Abstract.html)\n\n220. 陈K、牛M、陈Q。**用于视频求职面试中答案转录自动评分的层次化推理图神经网络**。AAAI 2021。[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.11960)\n\n221. 杨亮等。**图卷积神经网络中的属性为何会传播？**。AAAI 2021，[论文](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F16588)\n\n222. 傅学洋等。**基于双图卷积网络的雨线去除**。AAAI 2021，[论文](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F16224)\n\n223. 裴仁焕等。**用于行人轨迹预测的解耦多关系图卷积网络**。AAAI 2021，[论文](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F16174)\n\n224. 夏鑫等。**用于会话式推荐的自监督超图卷积网络**。AAAI 2021，[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.06852)\n\n225. 万盛等。**用于基于图的半监督学习的对比与生成式图卷积网络**。AAAI 2021，[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.07111)\n\n226. 陈展等。**用于基于骨骼的动作识别的多尺度时空图卷积网络**。AAAI 2021，[论文](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F16197)\n\n227. 陈欣等。**用图卷积网络拟合权重共享NAS的搜索空间**。AAAI 2021，[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2004.08423)\n\n228. 黄庆宝等。**基于依存句法树的多层级图卷积网络的故事结局生成**。AAAI 2021，[论文](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F17545)\n\n229. 常恒等。**增强！通过图幂运算构建鲁棒图卷积网络**。AAAI 2021，[论文](https:\u002F\u002Fpeople.eecs.berkeley.edu\u002F~sojoudi\u002FRobust_GCN.pdf)\n\n230. 博德宇等。**超越图卷积网络中的低频信息**。AAAI 2021，[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2101.00797)\n\n231. 杨涵等。**重新思考图神经网络的图正则化**。AAAI 2021，[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.02027)\n\n232. 赵彤等。**图神经网络的数据增强**。AAAI 2021，[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.06830)\n\n233. 游家轩等。**身份感知图神经网络**。AAAI 2021，[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2101.10320)\n\n234. 陆元福等。**学习如何预训练图神经网络**。AAAI 2021，[论文](http:\u002F\u002Fshichuan.org\u002Fdoc\u002F101.pdf)\n\n235. 李Q等。**使用近似梯度下降学习图神经网络**。AAAI 2021，[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.03429)\n\n236. 吴远凯等。**用于时空克里金插值的归纳式图神经网络**。AAAI 2021，[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.07527)\n\n237. 朱炯等。**具有异质性的图神经网络**。AAAI 2021，[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.13566)\n\n238. 李孟章等。**用于交通流量预测的时空融合图神经网络**。AAAI 2021，[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.09641)\n\n239. 钱胜胜等。**用于多标签跨模态检索的双重对抗图神经网络**。AAAI 2021，[论文](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F16345)\n\n240. 李孟章等。**基于图神经网络的多变量时间序列异常检测**。AAAI 2021，[论文](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F16523)\n\n241. 周凡等。**利用经验回放克服图神经网络中的灾难性遗忘**。AAAI 2021，[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.06002)\n\n242. 格奥尔吉奥斯·帕纳戈普洛斯等。**用于疫情预测的迁移图神经网络**。AAAI 2021，[论文](https:\u002F\u002Fwww.researchgate.net\u002Fpublication\u002F344294392_Transfer_Graph_Neural_Networks_for_Pandemic_Forecasting)\n\n243. 乌代·尚卡尔·桑塔马卢等。**通过不确定性匹配图神经网络防御投毒攻击**。AAAI 2021，[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2009.14455)\n\n244. 赵佳楠等。**图神经网络的异构图结构学习**。AAAI 2021，[论文](http:\u002F\u002Fshichuan.org\u002Fdoc\u002F100.pdf)\n\n245. 张亚楠等。**PC-RGNN：点云补全与图神经网络结合用于3D目标检测**。AAAI 2021，[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.10412)\n\n246. 宋腾飞等。**用于面部动作单元检测的不确定性图神经网络**。AAAI 2021，[论文](https:\u002F\u002Fwww.researchgate.net\u002Fpublication\u002F346853340_Uncertain_Graph_Neural_Networks_for_Facial_Action_Unit_Detection)\n\n247. 李孙等。**用于建模动态图的双曲变分图神经网络**。AAAI 2021，[论文](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F16563))\n\n248. 王冰辉等。**图上的半监督节点分类：马尔可夫随机场与图神经网络的比较**。AAAI 2021，[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.13085)\n\n249. 阿里吉特·塞哈诺比什等。**利用自监督边特征和图神经网络洞察SARS-CoV-2感染及COVID-19严重程度**。AAAI 2021，[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2006.12971)\n\n250. 乌特卡什·德赛等。**用于重构单体应用的图神经网络以剔除异常值**。AAAI 2021，[论文](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F16079))\n\n251. 刘慧慧等。**克服图神经网络中的灾难性遗忘问题**。AAAI 2021，[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.06002)\n\n252. 姚宇航等。**基于循环图神经网络的动态图可解释聚类**。AAAI 2021，[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.08740)\n\n253. 刘大宗等。**基于时空图神经网络的掩码重建用于视频目标分割**。AAAI 2021，[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.05499)\n\n254. Cai T等。**Graphnorm：一种加速图神经网络训练的原则性方法**。ICML 2021。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2009.03294.pdf)\n\n255. Baranwal A等。**半监督分类中的图卷积：提升线性可分性和分布外泛化能力**。ICML 2021。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2102.06966.pdf)\n\n256. Hang M等。**一种增强GNN表达能力的集体学习框架**。ICML 2021。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2003.12169.pdf)\n\n257. Henderson R等。**通过正交化和诱导稀疏性提升分子图神经网络的可解释性**。ICML 2021。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2105.04854.pdf)\n\n258. Fey M等。**GNNAutoScale：基于历史嵌入的可扩展且富有表现力的图神经网络**。ICML 2021。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2106.05609.pdf)\n\n259. 谢国森等。**面向少样本语义分割的尺度感知图神经网络**。CVPR 2021。[论文](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fpapers\u002FXie_Scale-Aware_Graph_Neural_Network_for_Few-Shot_Semantic_Segmentation_CVPR_2021_paper.pdf)\n\n260. 凯·费舍尔等。**StickyPillars：利用图神经网络在点云上进行鲁棒高效特征匹配**。CVPR 2021。[论文](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fpapers\u002FFischer_StickyPillars_Robust_and_Efficient_Feature_Matching_on_Point_Clouds_Using_CVPR_2021_paper.pdf)\n\n261. 杨一丁等。**通过图神经网络学习动力学以实现人体姿态估计与跟踪**。CVPR 2021。[论文](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fpapers\u002FYang_Learning_Dynamics_via_Graph_Neural_Networks_for_Human_Pose_Estimation_CVPR_2021_paper.pdf)\n\n262. 吉约姆·若梅等。**在计算病理学中量化图神经网络的解释器**。CVPR 2021。[论文](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fpapers\u002FJaume_Quantifying_Explainers_of_Graph_Neural_Networks_in_Computational_Pathology_CVPR_2021_paper.pdf)\n\n263. 蔡绍飞等。**从消息传递视角重新思考图神经架构搜索**。CVPR 2021。[论文](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fpapers\u002FCai_Rethinking_Graph_Neural_Architecture_Search_From_Message-Passing_CVPR_2021_paper.pdf)\n\n264. 京永成等。**融合来自异构图神经网络的知识**。CVPR 2021。[论文](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fpapers\u002FJing_Amalgamating_Knowledge_From_Heterogeneous_Graph_Neural_Networks_CVPR_2021_paper.pdf)\n\n265. 梅迪·巴赫里等。**二值图神经网络**。CVPR 2021。[论文](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fpapers\u002FBahri_Binary_Graph_Neural_Networks_CVPR_2021_paper.pdf)\n\n266. 石刘帅等。**SGCN：用于行人轨迹预测的稀疏图卷积网络**。CVPR 2021。[论文](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fpapers\u002FShi_SGCN_Sparse_Graph_Convolution_Network_for_Pedestrian_Trajectory_Prediction_CVPR_2021_paper.pdf)\n\n267. 佘东宇等。**面向统一美学评估的层次布局感知图卷积网络**。CVPR 2021。[论文](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fpapers\u002FShe_Hierarchical_Layout-Aware_Graph_Convolutional_Network_for_Unified_Aesthetics_Assessment_CVPR_2021_paper.pdf)\n\n268. 戴金斗等。**一种双曲到双曲的图卷积网络**。CVPR 2021。[论文](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fpapers\u002FDai_A_Hyperbolic-to-Hyperbolic_Graph_Convolutional_Network_CVPR_2021_paper.pdf)\n\n269. 王俊富等。**Bi-GCN：二值图卷积网络**。CVPR 2021。[论文](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fpapers\u002FWang_Bi-GCN_Binary_Graph_Convolutional_Network_CVPR_2021_paper.pdf)\n\n270. 拉兹万·卡拉马拉乌等。**用于主动学习的序列图卷积网络**。CVPR 2021。[论文](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2021\u002Fpapers\u002FCaramalau_Sequential_Graph_Convolutional_Network_for_Active_Learning_CVPR_2021_paper.pdf)\n\n271. 徐克玉等。**神经网络如何外推：从前馈网络到图神经网络**。ICLR 2021。[论文](https:\u002F\u002Fopenreview.net\u002Fpdf?id=UH-cmocLJC)\n\n272. 瓦伊斯·阿齐齐安等。**不变与等变图神经网络的表达能力**。ICLR 2021。[论文](https:\u002F\u002Fopenreview.net\u002Fpdf?id=lxHgXYN4bwl)\n\n\n\n### ![#1589F0](https:\u002F\u002Fvia.placeholder.com\u002F15\u002F1589F0\u002F000000?text=+) `2022年会议论文进展`:\n----------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n**2022年提出的新型GNN方法**\n\n274. **向黑暗学习：利用多样化的负样本提升图卷积神经网络性能**。AAAI 2022。[论文](https:\u002F\u002Fwww.aaai.org\u002FAAAI22Papers\u002FAAAI-4442.DuanW.pdf)\n\n274. **块建模引导的图卷积神经网络**。AAAI 2022。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2112.13507.pdf)\n\n275. **可变形图卷积神经网络**。AAAI 2022。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2112.14438.pdf)\n\n276. **ProtGNN：迈向自解释的图神经网络**。AAAI 2022。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2112.00911.pdf)\n\n277. **自适应核图神经网络**。AAAI 2022。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2112.04575.pdf)\n\n278. **通过多样化和交互式消息传递实现自监督图神经网络**。AAAI 2022。[论文](https:\u002F\u002Fyangliang.github.io\u002Fpdf\u002Faaai22.pdf)\n\n279. **一种自监督的混合曲率图神经网络**。AAAI 2022。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2112.05393.pdf)\n\n280. **KerGNNs：具有图核的可解释图神经网络**。AAAI 2022。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2201.00491.pdf)\n\n281. **正交图神经网络**。AAAI 2022。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2109.11338.pdf)\n\n282. **SAIL：自增强图对比学习**。AAAI 2022。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2009.00934.pdf)\n\n283. **AutoGCL：基于可学习视图生成器的自动化图对比学习**。AAAI 2022。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2109.10259.pdf)\n\n284. **带有信息正则化的对抗性图对比学习**。WWW 2022。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.06491.pdf)\n\n285. **置信度可能具有欺骗性：分布偏移下图神经网络的自训练**。WWW 2022。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2201.11349.pdf)\n\n286. **曲率图生成对抗网络**。WWW 2022。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.01604.pdf)\n\n287. **双空间图对比学习**。WWW 2022。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2201.07409.pdf)\n\n288. **GBK-GNN：用于同时建模同质性和异质性的门控双核图神经网络**。WWW 2022。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2110.15777.pdf)\n\n289. **通过最大化速率降低进行几何图表示学习**。WWW 2022。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.06241.pdf)\n\n290. **图社区对比学习**。WWW 2022。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2110.14863.pdf)\n\n291. **超越属性与拓扑之间权衡的图神经网络**。WWW 2022。[论文](https:\u002F\u002Fyangliang.github.io\u002Fpdf\u002Fwww22.pdf)\n\n292. **面向图神经网络的图适应型修正线性单元**。WWW 2022。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.06281.pdf)\n\n293. **元权重图神经网络：突破全局同质性的极限**。WWW 2022。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.06281.pdf)\n\n294. **极化图神经网络**。WWW 2022。[暂不可用]\n\n295. **关于图神经网络中以规模为导向的长尾图分类问题**。WWW 2022。[论文](https:\u002F\u002Fzemin-liu.github.io\u002Fpapers\u002FSOLT-GNN-WWW-22.pdf)\n\n296. **SimGRACE：一种无需数据增强的简单图对比学习框架**。WWW 2022。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.03104.pdf)\n\n297. **迈向无监督深度图结构学习**。WWW 2022。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2201.06367.pdf)\n\n298. **图神经网络的表达能力和近似性质**。ICLR 2022。[论文](https:\u002F\u002Fopenreview.net\u002Fpdf?id=wIzUeM3TAU)\n\n299. **“图神经网络如何超越Weisfeiler-Lehman测试？”的新视角**。ICLR 2022。[论文](https:\u002F\u002Fopenreview.net\u002Fpdf?id=uxgg9o7bI_3)\n\n299. **基于p-拉普拉斯算子的图神经网络**。ICML 2022。[论文](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Ffu22e\u002Ffu22e.pdf)\n\n299. **深入研究置换敏感的图神经网络**。ICML 2022。[论文](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fhuang22l\u002Fhuang22l.pdf)\n\n299. **具有完整局部坐标系的SE(3)等变图神经网络**。ICML 2022。[论文](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fdu22e\u002Fdu22e.pdf)\n\n299. **图神经网络中谱效应的新视角**。ICML 2022。[论文](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fyang22n\u002Fyang22n.pdf)\n\n299. **谱图神经网络有多强大？**。ICML 2022。[论文](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fwang22am\u002Fwang22am.pdf)\n\n299. **图神经网络的局部增强**。ICML 2022。[论文](https:\u002F\u002Fproceedings.mlr.press\u002Fv162\u002Fliu22s\u002Fliu22s.pdf)\n\n299. **图神经网络训练与数据分层**。KDD 2022。[论文](https:\u002F\u002Fdl-acm-org.ndfpz.top\u002Fdoi\u002Fpdf\u002F10.1145\u002F3534678.3539038)\n\n299. **模型退化阻碍了深度图神经网络的发展**。KDD 2022。[论文](https:\u002F\u002Fdl-acm-org.geray.top\u002Fdoi\u002Fpdf\u002F10.1145\u002F3534678.3539374)\n\n299. **通过新型二阶连续图神经网络改进社交网络嵌入**。KDD 2022。[论文](https:\u002F\u002Fdl-acm-org.geray.top\u002Fdoi\u002Fpdf\u002F10.1145\u002F3534678.3539415)\n\n299. **具有节点级架构的图神经网络**。KDD 2022。[论文](https:\u002F\u002Fdl-acm-org.geray.top\u002Fdoi\u002Fpdf\u002F10.1145\u002F3534678.3539387)\n\n299. **GPPT：图预训练与提示调优以泛化图神经网络**。KDD 2022。[论文](https:\u002F\u002Fdl-acm-org.geray.top\u002Fdoi\u002Fpdf\u002F10.1145\u002F3534678.3539249)\n\n299. **异质性如何影响图神经网络的鲁棒性？理论联系与实践启示**。KDD 2022。[论文](https:\u002F\u002Fdl-acm-org.geray.top\u002Fdoi\u002Fpdf\u002F10.1145\u002F3534678.3539418)\n\n299. **通过缓解敏感属性泄露来提升图神经网络的公平性**。KDD 2022。[论文](https:\u002F\u002Fdl-acm-org.ndfpz.top\u002Fdoi\u002Fpdf\u002F10.1145\u002F3534678.3539404)\n\n299. **层次扩散散射图神经网络**。IJCAI 2022。[论文](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F0519.pdf)\n\n299. **RAW-GNN：基于随机游走聚合的图神经网络**。IJCAI 2022。[论文](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F0293.pdf)\n\n299. **图神经网络加速综述：算法视角**。IJCAI 2022。[论文](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F0772.pdf)\n\n299. **用于隐私保护节点分类的垂直联邦图神经网络**。IJCAI 2022。[论文](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2022\u002F0272.pdf)\n\n\n\n\n\n\n----------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n**2022年提出的新型基于GNN的应用**\n\n318. **用于少样本学习的混合图神经网络**。AAAI 2022。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2112.06538.pdf)\n\n299. **利用图神经网络辅助蒙特卡洛树搜索进行量子比特路由**。AAAI 2022。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2104.01992.pdf)\n\n299. **CausalGNN：基于因果关系的图神经网络用于时空流行病预测**。AAAI 2022。[论文](https:\u002F\u002Fwww.aaai.org\u002FAAAI22Papers\u002FAISI-6475.WangL.pdf)\n\n299. **LUNAR：通过图神经网络统一局部异常检测方法**。AAAI 2022。[论文](https:\u002F\u002Fwww.aaai.org\u002FAAAI22Papers\u002FAAAI-51.GoodgeA.pdf)\n\n299. **DDGCN：用于社交媒体谣言检测的双动态图卷积网络**。AAAI 2022。[论文](https:\u002F\u002Fwww.aaai.org\u002FAAAI22Papers\u002FAAAI-6370.SunM.pdf)\n\n299. **用于推荐的低通图卷积网络**。AAAI 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Dai Quoc Nguyen、Tu Dinh Nguyen、Dinh Phung。**用于图分类的通用自注意力网络**。arXiv 2020。[论文](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1909.11855.pdf)。\n\n\n\n## ![#f03c15](https:\u002F\u002Fvia.placeholder.com\u002F15\u002Ff03c15\u002F000000?text=+) `GNN 开源平台`：\n\n1. **Deep Graph Library（DGL）**\n\n   DGL 由纽约大学、上海纽约大学、AWS 上海研究院以及 AWS MXNet 科研团队开发并维护。\n\n   发起时间：2018 年。\n\n   来源：[网址](https:\u002F\u002Fwww.dgl.ai\u002F)，[GitHub](https:\u002F\u002Fgithub.com\u002Fjermainewang\u002Fdgl)\n\n2. **NGra**\n\n   NGra 由北京大学和微软亚洲研究院开发并维护。\n\n   发起时间：2018 年。\n\n   来源：[PDF](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1810.08403.pdf)\n\n3. **Graph_nets**\n\n   Graph_nets 由 [DeepMind](https:\u002F\u002Fdeepmind.com\u002F) 和 Google 公司开发并维护。\n\n   发起时间：2018 年。\n\n   来源：[GitHub](https:\u002F\u002Fgithub.com\u002Fdeepmind\u002Fgraph_nets)\n\n4. **Euler**\n\n   Euler 由阿里巴巴集团旗下的阿里妈妈开发并维护。\n\n   发起时间：2019 年。\n\n   来源：[GitHub](https:\u002F\u002Fgithub.com\u002Falibaba\u002Feuler)\n\n5. **PyTorch Geometric**\n\n   PyTorch Geometric 由德国多特蒙德工业大学开发并维护。\n\n   发起时间：2019 年。\n\n   来源：[GitHub](https:\u002F\u002Fgithub.com\u002Frusty1s\u002Fpytorch_geometric)，[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1903.02428?context=cs.LG)\n\n6. **PyTorch-BigGraph（PBG）**\n\n   PBG 由 Facebook AI Research 开发并维护。\n\n   发起时间：2019 年。\n\n   来源：[GitHub](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002FPyTorch-BigGraph)，[论文](https:\u002F\u002Farxiv.org\u002Fabs\u002F1903.12287)\n\n7. **Angel**\n\n   Angel 由腾讯公司开发并维护。\n\n   发起时间：2019 年。\n\n   来源：[GitHub](https:\u002F\u002Fgithub.com\u002FAngel-ML\u002Fangel)\n\n8. **Plato**\n\n   Plato 由腾讯公司开发并维护。\n\n   发起时间：2019 年。\n\n   来源：[GitHub](https:\u002F\u002Fgithub.com\u002Ftencent\u002Fplato)\n\n9. **PGL**\n\n   PGL 由百度公司开发并维护。\n\n   发起时间：2019 年。\n\n   来源：[GitHub](https:\u002F\u002Fgithub.com\u002FPaddlePaddle\u002FPGL)\n\n10. **OGB**\n\n    Open Graph Benchmark（OGB）由斯坦福大学开发并维护。\n\n    发起时间：2019 年。\n\n    来源：[GitHub](http:\u002F\u002Fogb.stanford.edu)\n\n11. **Benchmarking GNNs**\n\n    Benchmarking GNNs 由南洋理工大学开发并维护。\n\n    发起时间：2020 年。\n\n    来源：[GitHub](https:\u002F\u002Fgithub.com\u002Fgraphdeeplearning\u002Fbenchmarking-gnns)\n\n12. **Graph-Learn**\n\n    Graph-Learn 由阿里巴巴集团开发并维护。\n\n    发起时间：2020 年。\n\n    来源：[GitHub](https:\u002F\u002Fgithub.com\u002Falibaba\u002Fgraph-learn)\n\n13. **AutoGL（自动图学习）** 新项目\n\n    AutoGL 由清华大学开发并维护。\n\n    发起时间：2020 年。\n\n    来源：[GitHub](https:\u002F\u002Fgithub.com\u002FTHUMNLab\u002FAutoGL)\n\n\n\n## ![#f03c15](https:\u002F\u002Fvia.placeholder.com\u002F15\u002Ff03c15\u002F000000?text=+) `开胃菜：网络\u002F图结构数据的艺术展览`：\n\n  ![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjdlc105_Must-read-papers-and-continuous-tracking-on-Graph-Neural-Network-GNN-progress_readme_cc0b37d5a8a2.gif)\n \n 1. **有趣的社交网络**。\n\n  ![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjdlc105_Must-read-papers-and-continuous-tracking-on-Graph-Neural-Network-GNN-progress_readme_1eef02605e3e.jpg)\n \n 2. **生物网络之美**。","# Graph Neural Network (GNN) 必读论文与进展追踪快速上手指南\n\n本项目并非一个可安装的软件库或框架，而是一个**持续更新的 GNN 领域必读论文清单与研究进展追踪仓库**。它旨在为研究人员和开发者提供从基础综述到前沿应用的核心文献索引。\n\n以下是如何高效利用该资源进行学习和研究的指南。\n\n## 1. 环境准备\n\n由于本项目主要包含论文列表、链接及说明文档，无需复杂的深度学习环境（如 GPU、CUDA 等）即可浏览核心内容。但若您计划复现列表中提到的代码，建议准备以下基础环境：\n\n*   **操作系统**: Windows, macOS 或 Linux\n*   **必备工具**:\n    *   Git (用于克隆仓库)\n    *   现代浏览器 (访问论文链接)\n    *   PDF 阅读器\n*   **可选依赖 (用于复现论文代码)**:\n    *   Python 3.7+\n    *   PyTorch 或 TensorFlow (根据具体论文要求)\n    *   DGL (Deep Graph Library) 或 PyTorch Geometric\n\n## 2. 获取与安装步骤\n\n本项目通过 Git 仓库形式发布，获取最新论文列表的步骤如下：\n\n### 步骤 1: 克隆仓库\n打开终端或命令行工具，执行以下命令将项目拉取到本地：\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002FBlucezhang\u002FMust-read-papers-and-continuous-tracking-on-Graph-Neural-Network-GNN-progress.git\n```\n\n> **国内加速建议**: 如果直接克隆速度较慢，可使用国内镜像源（如 Gitee 镜像，若有）或配置代理。\n> 例如使用镜像地址（需确认是否有同步镜像，若无则使用原地址）：\n> ```bash\n> git clone https:\u002F\u002Fgitee.com\u002Fmirror_username\u002FMust-read-papers-and-continuous-tracking-on-Graph-Neural-Network-GNN-progress.git\n> ```\n\n### 步骤 2: 进入目录\n```bash\ncd Must-read-papers-and-continuous-tracking-on-Graph-Neural-Network-GNN-progress\n```\n\n### 步骤 3: 查看更新\n该项目持续更新，建议定期拉取最新内容：\n```bash\ngit pull origin main\n```\n\n## 3. 基本使用\n\n本项目的核心用法是**按图索骥**，根据研究需求查阅 `README.md` 中列出的论文链接。\n\n### 场景 A: 快速入门 GNN 理论\n如果您是初学者，建议优先阅读 `Survey papers` (综述论文) 部分。这些文章系统性地梳理了 GNN 的发展脉络。\n\n**操作示例**:\n1. 打开本地的 `README.md` 文件。\n2. 定位到 `Survey papers` 章节。\n3. 点击推荐的首选综述链接（例如）：\n   *   **题目**: Graph Neural Networks: A Review of Methods and Applications\n   *   **链接**: [https:\u002F\u002Farxiv.org\u002Fpdf\u002F1812.08434.pdf](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1812.08434.pdf)\n   *   **用途**: 了解 GNN 的基本分类、方法及应用场景。\n\n### 场景 B: 追踪特定领域的最新进展\n如果您关注特定应用（如推荐系统、生物信息学），可搜索 `Journal papers` 或关键词。\n\n**操作示例**:\n假设您想研究 **药物发现 (Drug Discovery)** 相关的 GNN 应用：\n1. 在 `README.md` 中搜索关键词 \"Drug\" 或 \"Chemical\"。\n2. 找到相关论文条目，例如：\n   *   **题目**: A graph-convolutional neural network model for the prediction of chemical reactivity\n   *   **来源**: Chemical Science, 2019\n   *   **行动**: 点击下载链接阅读正文，并通常可在论文末尾或项目主页找到对应的 GitHub 代码仓库链接进行复现。\n\n### 场景 C: 关注里程碑式工作\n项目中特别标注了引用率极高的代表性工作，适合深入精读。\n\n**重点推荐**:\n*   **论文**: Semi-supervised classification with graph convolutional networks (GCNs)\n*   **作者**: T.N. Kipf and M. Welling (ICLR 2017)\n*   **地位**: GNN 领域的奠基之作，引用次数极高（截至 2023 年已超 2 万次）。\n*   **建议**: 所有 GNN 研究者的必读起点。\n\n---\n**提示**: 项目由 Allen Bluce 和 Anne Bluce 维护，如有论文遗漏或错误，可通过邮件 `jdlc105@qq.com` 或 `lbtjackbluce@gmail.com` 联系贡献者。","某生物制药公司的算法团队正致力于利用图神经网络（GNN）加速新药分子筛选，急需掌握该领域的最新突破以优化模型架构。\n\n### 没有 Must-read-papers-and-continuous-tracking-on-Graph-Neural-Network-GNN-progress 时\n- **文献检索如大海捞针**：研究人员需在 arXiv、IEEE 等多个平台手动搜索，难以区分哪些是像 GCN 那样被引用两万多次的奠基性论文，哪些是昙花一现的尝试。\n- **技术视野存在盲区**：容易局限于熟悉的社交网络应用，忽略 GNN 在生物信息学或化学信息学中的跨界创新，导致模型设计思路狭窄。\n- **跟进进度严重滞后**：缺乏持续追踪机制，往往错过 ICML、KDD 等顶会的最新成果，重复造轮子或采用已过时的技术方案。\n- **综述资料分散零碎**：找不到系统性的综述文章（Survey papers），新人入门需花费数周时间拼凑知识体系，拖慢项目启动速度。\n\n### 使用 Must-read-papers-and-continuous-tracking-on-Graph-Neural-Network-GNN-progress 后\n- **核心文献一键直达**：直接获取按影响力排序的必读清单，快速定位 Kipf 等人的高引经典及最新变体，将文献调研时间从数周缩短至几天。\n- **跨域灵感即时激发**：通过分类清晰的应用案例（如药物发现、3D 点云），迅速将其他领域的成功范式迁移到分子图建模中，提升模型创新性。\n- **前沿动态实时同步**：依托项目持续的更新记录，第一时间掌握来自 2022-2023 年顶会的最新进展，确保技术栈始终处于行业最前沿。\n- **知识体系系统化构建**：利用收录的高质量综述论文，团队成员能快速建立完整的 GNN 认知框架，大幅降低学习门槛并统一技术语言。\n\n该资源库通过将分散的顶尖成果系统化与动态化，帮助研发团队在激烈的新药竞赛中显著缩短了从理论调研到模型落地的周期。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjdlc105_Must-read-papers-and-continuous-tracking-on-Graph-Neural-Network-GNN-progress_6be78f71.png","jdlc105","Allen Bluce","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002Fjdlc105_e846c282.jpg","PhD Student,\r\n\r\nInterests: Deep learning on complex graph data, such as graph embedding, GNN, GCNN.\r\n\r\nCollege of computer science and technology, NUAA","Nanjing University of Aeronautics and Astronautics","Nanjing, China",null,"https:\u002F\u002Fgithub.com\u002Fjdlc105",767,105,"2026-03-28T04:20:51","","未说明",{"notes":90,"python":88,"dependencies":91},"该项目是一个图神经网络（GNN）领域的必读论文清单和进展追踪仓库，主要包含论文列表、链接及引用统计信息。README 中未提供任何代码实现、安装指南或具体的运行环境需求（如操作系统、GPU、内存、Python 版本或依赖库）。用户仅需通过浏览器阅读文档或下载论文，无需配置特定的计算环境。",[],[18],"2026-03-27T02:49:30.150509","2026-04-11T03:26:40.472965",[],[]]