{"id":13570713,"url":"https://github.com/zlpure/awesome-graph-representation-learning","last_synced_at":"2025-04-04T07:31:53.025Z","repository":{"id":153541301,"uuid":"386132833","full_name":"zlpure/awesome-graph-representation-learning","owner":"zlpure","description":"A curated list for awesome graph representation learning resources.","archived":false,"fork":false,"pushed_at":"2022-11-22T16:26:04.000Z","size":22,"stargazers_count":148,"open_issues_count":0,"forks_count":27,"subscribers_count":5,"default_branch":"main","last_synced_at":"2024-05-21T17:09:52.140Z","etag":null,"topics":["deep-learning","graph-representation-learning"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/zlpure.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2021-07-15T02:07:50.000Z","updated_at":"2024-04-21T10:59:54.000Z","dependencies_parsed_at":"2023-05-19T04:15:29.866Z","dependency_job_id":null,"html_url":"https://github.com/zlpure/awesome-graph-representation-learning","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zlpure%2Fawesome-graph-representation-learning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zlpure%2Fawesome-graph-representation-learning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zlpure%2Fawesome-graph-representation-learning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zlpure%2Fawesome-graph-representation-learning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/zlpure","download_url":"https://codeload.github.com/zlpure/awesome-graph-representation-learning/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246989539,"owners_count":20865317,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["deep-learning","graph-representation-learning"],"created_at":"2024-08-01T14:00:54.421Z","updated_at":"2025-04-04T07:31:53.000Z","avatar_url":"https://github.com/zlpure.png","language":null,"funding_links":[],"categories":["Machine Learning Algorithms","Related Awesome","Table of Contents","Other Lists"],"sub_categories":["Sparsity and Popularity Biases","Pre-Print Status","TeX Lists"],"readme":"# Awesome Deep Graph Representation Learning\n\n[![Awesome](https://awesome.re/badge.svg)](https://awesome.re) ![visitors](https://visitor-badge.glitch.me/badge?page_id=zlpure/awesome-graph-representation-learning) ![GitHub stars](https://img.shields.io/github/stars/zlpure/awesome-graph-representation-learning.svg?color=green)  ![GitHub forks](https://img.shields.io/github/forks/zlpure/awesome-graph-representation-learning?color=9cf)\n\n\u003cp align=\"center\"\u003e\n  \u003cimg width=\"250\" src=\"https://camo.githubusercontent.com/1131548cf666e1150ebd2a52f44776d539f06324/68747470733a2f2f63646e2e7261776769742e636f6d2f73696e647265736f726875732f617765736f6d652f6d61737465722f6d656469612f6c6f676f2e737667\" \"Awesome!\"\u003e\n\u003c/p\u003e\n\nA curated list for awesome deep graph representation learning resources. Inspired by [awesome-deep-learning-papers](https://github.com/terryum/awesome-deep-learning-papers), [awesome-deep-vision](https://github.com/kjw0612/awesome-deep-vision), [awesome-architecture-search](https://github.com/markdtw/awesome-architecture-search), [awesome-self-supervised-learning-for-graphs](https://github.com/SXKDZ/awesome-self-supervised-learning-for-graphs), and [awesome-deep-gnn](https://github.com/mengliu1998/awesome-deep-gnn).\n# Background\n\u003e The field of graph representation learning has grown at an incredible (and sometimes unwieldy) pace over the past seven years, transforming from a small subset of researchers working on a relatively niche topic to one of the fastest growing sub-areas of deep learning.  \u0026#160;\u0026#160;\u0026#160;\u0026#160; - - William L. Hamilton\n\nGraph representation learning (GRL) have recently become increasingly popular due to their ability to model *relationships* or *interactions* of complex systems. However GRL is still a nascent field in the Machine Learning community. Rather than providing overwhelming amount of papers, the goal of this repository is to provide a *curated list* of awesome GRL papers in recent top conference that we have read, as well as some intriguing blog posts and talks.\n# Contributing\nYou are welcome to contribute this repo by contracting [me](zengl18@mails.tsinghua.edu.cn) or adding [pull request](https://github.com/zlpure/awesome-graph-representation-learning/pulls).\n\nMarkdown formart:\n```markdown\nPaper Name [[pdf]](link) [[code]](link)\n\nAuthor 1, Author 2, Author 3. \n\nConference Year\n\n*Taxonomy* (No more than 5 words)\n```\n# Table of Contents\n- [Papers](#papers)\n    - [Surveys](#surveys)\n    - [ICML 2022](#ICML-2022)\n    - [ICLR 2022](#ICLR-2022)\n    - [WWW 2022](#WWW-2022)\n    - [NeurIPS 2021](#NeurIPS-2021)\n    - [KDD 2021](#KDD-2021)\n    - [ICML 2021](#ICML-2021)\n    - [WWW 2021](#WWW-2021)\n    - [ICLR 2021](#ICLR-2021)\n    - [NeurIPS 2020](#NeurIPS-2020)\n    - [KDD 2020](#KDD-2020)\n    - [AAAI 2021](#AAAI-2021)\n    - [ICML 2020](#ICML-2020)\n    - [ICLR 2020](#ILCR-2020)\n    - [NeurIPS 2019](#NeurIPS-2019)\n    - [Some Must-Read Papers](#some-must-read-papers)\n- [Talks](#Talks)\n- [Blog posts](#Blog-posts)\n\n\n# Papers\n## Surveys\n- Graph Representation Learning [[pdf]](https://www.cs.mcgill.ca/~wlh/grl_book/files/GRL_Book.pdf)\n\n    William L. Hamilton\n\n    Book\n\n    *Classical survey*\n    \n- Networks, Crowds, and Markets - Reasoning About a Highly Connected World [[pdf]](https://www.cs.cornell.edu/home/kleinber/networks-book/networks-book.pdf)\n\n    D Easley, J Kleinberg\n\n    Book\n\n    *Basic concepts on Networks*\n\n- Network Science [[pdf]](http://networksciencebook.com/chapter/0)\n\n    Albert-László Barabási\n\n    Book\n\n    *Basic concepts on Networks*\n\n- Relational inductive biases, deep learning, and graph networks [[pdf]](https://arxiv.org/pdf/1806.01261.pdf)\n\n    Battaglia, Peter W and Hamrick, Jessica B, et al.\n\n    Arxiv 2018\n\n    *Relational inductive biases on graphs*\n\n- A comprehensive survey on graph neural networks [[pdf]](https://arxiv.org/pdf/1901.00596.pdf)\n    \n    Zonghan Wu, Shirui Pan, Chen, Guodong Long, Chengqi Zhang, Philip, S Yu\n\n    IEEE 2020\n\n    *Survey*\n\n- Self-Supervised Learning of Graph Neural Networks: A Unified Review [[pdf]](https://arxiv.org/pdf/2102.10757.pdf)\n\n    Yaochen Xie, Zhao Xu, Jingtun Zhang, Zhengyang Wang, Shuiwang Ji\n\n    Arxiv 2021\n\n    *Self-supervised learning*\n\n- Combinatorial optimization and reasoning with graph neural networks [[pdf]](https://arxiv.org/pdf/2102.09544.pdf)\n\n    Quentin Cappart, Didier Chételat, Elias Khalil, Andrea Lodi, Christopher Morris, Petar Veličković\n\n    IJCAI 2021\n\n    *Survey on GNNs for combinatorial optimization and algorithmic reasoning*\n\n## ICML 2022\n- 3D Infomax improves GNNs for Molecular Property Prediction [[pdf]](https://arxiv.org/abs/2110.04126.pdf) [[code]](https://github.com/HannesStark/3DInfomax)\n\n    Hannes Stärk, Dominique Beaini, Gabriele Corso, et al.\n\n    *Molecular property prediction on 3D molecule geometry*\n\n-  EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction [[pdf]](https://arxiv.org/pdf/2202.05146.pdf) [[code]](https://github.com/HannesStark/EquiBind)\n\n    Hannes Stärk, Octavian-Eugen Ganea, et al.\n\n    *Drug-protein binding prediction*\n\n- G-Mixup: Graph Data Augmentation for Graph Classification [[pdf]](https://arxiv.org/pdf/2202.07179.pdf) [[code]](https://github.com/ahxt/g-mixup)\n\n    Xiaotian Han, Zhimeng Jiang, Ninghao Liu, Xia Hu\n\n    *Mixup on graphs*\n\n## ICLR 2022\n- Context-Aware Sparse Deep Coordination Graphs [[pdf]](https://arxiv.org/abs/2106.02886.pdf) [[code]](https://github.com/TonghanWang/CASEC-MACO-benchmark)\n\n    Tonghan Wang, Liang Zeng, et al.\n\n    *Coordination graphs on multi-agent RL*\n\n- On the Unreasonable Effectiveness of Feature propagation in Learning on Graphs with Missing Node Features [[pdf]](https://arxiv.org/pdf/2111.12128.pdf)\n\n    Emanuele Rossi, Henry Kenlay, et al.\n\n    *Feature propagation on graphs*\n\n- Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods [[pdf]](https://arxiv.org/pdf/2111.04840.pdf) [[code]](https://github.com/amazon-research/gnn-tail-generalization)\n\n    Wenqing Zheng, Edward W Huang, et al.\n\n    *Imbalanced learning on graphs*\n\n- Equivariant Graph Mechanics Networks with Constraints [[pdf]](https://arxiv.org/abs/2203.06442.pdf) [[code]](https://github.com/hanjq17/GMN)\n\n    Wenbing Huang, Jiaqi Han, et al.\n\n    *AI for science using GNNs*\n\n- Discovering Invariant Rationales for Graph Neural Networks [[pdf]](https://arxiv.org/pdf/2201.12872.pdf) [[code]](https://github.com/Wuyxin/DIR-GNN)\n\n    Ying-Xin Wu, Xiang Wang, An Zhang, Xiangnan He, Tat-Seng Chua\n\n    *Causal inference on graphs*\n\n- Is Homophily a Necessity for Graph Neural Networks? [[pdf]](https://arxiv.org/pdf/2106.06134.pdf)\n\n    Yao Ma, Xiaorui Liu, Neil Shah, Jiliang Tang\n\n    *Homophily property on GNNs*\n\n- Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design [[pdf]](https://arxiv.org/pdf/2110.04624.pdf) \n\n    Wengong Jin, Jeremy Wohlwend, Regina Barzilay, Tommi Jaakkola\n\n    *AI for drugs using GNNs*\n\n- Graph-Guided Network for Irregularly Sampled Multivariate Time Series [[pdf]](https://arxiv.org/pdf/2110.05357.pdf) [[code]](https://github.com/mims-harvard/Raindrop)\n\n    Xiang Zhang, Marko Zeman, Theodoros Tsiligkaridis, Marinka Zitnik\n\n    *Temporal-spatial data using GNNs*\n\n- Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and Practical Solutions [[pdf]](https://arxiv.org/pdf/2106.01098.pdf) [[code]](https://github.com/BorgwardtLab/ggme)\n\n    Leslie O'Bray, Max Horn, Bastian Rieck, Karsten Borgwardt\n\n    *Evaluation of graph generation*\n\n- Context-Aware Sparse Deep Coordination Graphs [[pdf]](https://arxiv.org/pdf/2106.02886.pdf) [[code]](https://github.com/TonghanWang/CASEC-MACO-benchmark)\n\n    Tonghan Wang, Liang Zeng, Weijun Dong, Qianlan Yang, Yang Yu, Chongjie Zhang\n\n    *Coordination graphs*\n\n\n## WWW 2022\n- Towards Unsupervised Deep Graph Structure Learning [[pdf]](https://arxiv.org/abs/2201.06367.pdf) [[code]](https://github.com/GRAND-Lab/SUBLIME)\n\n    Yixin Liu, Yu Zheng, Daokun Zhang, Hongxu Chen, Hao Peng, Shirui Pan\n\n    *Graph structure learning*\n\n- ClusterSCL: Cluster-Aware Supervised Contrastive Learning on Graphs [[pdf]](http://keg.cs.tsinghua.edu.cn/yuxiao/papers/WWW22-Wang-ClusterSCL.pdf) [[code]](https://github.com/wyl7/ClusterSCL)\n\n    Yanling Wang, Jing Zhang, et al.\n\n    *Graph contrastive learning*\n\n- ALLIE: Active Learning on Large-scale Imbalanced Graphs [[pdf]](https://dl.acm.org/doi/pdf/10.1145/3485447.3512229) \n\n    Limeng Cui, Xianfeng Tang, et al.\n\n    *Active learning \u0026 Imbalanced learning*\n\n- PaSca: A Graph Neural Architecture Search System under the Scalable Paradigm [[pdf]](https://arxiv.org/pdf/2203.00638.pdf) \u003cfont color=red\u003e(Best candidiate paper)\u003c/font\u003e\n\n    Wentao Zhang, Yu Shen, et al.\n\n    *Neural architecture search on graphs*\n\n## NeurIPS 2021\n- Multi-view Contrastive Graph Clustering [[pdf]](https://arxiv.org/pdf/2110.11842.pdf) [[code]](https://github.com/Panern/MCGC)\n\n    Erlin Pan, Zhao Kang\n\n    *Graph clustering*\n\n- Subgraph Federated Learning with Missing Neighbor Generation [[pdf]](https://arxiv.org/pdf/2106.13430.pdf)\n\n    Ke Zhang, Carl Yang, Xiaoxiao Li, Lichao Sun, Siu Ming Yiu\n\n    *Federated learning on graphs*\n\n- Edge Representation Learning with Hypergraphs [[pdf]](https://arxiv.org/pdf/2106.15845.pdf) [[code]](https://github.com/harryjo97/EHGNN)\n\n    Jaehyeong Jo, Jinheon Baek, Seul Lee, et al.\n\n    *Edge representation learning on graphs*\n\n- Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration [[pdf]](https://arxiv.org/pdf/2109.14285.pdf)\n\n    Xiao Wang, Hongrui Liu, Chuan Shi, Cheng Yang\n\n    *Confidence calibration of GNNs*\n\n- InfoGCL: Information-Aware Graph Contrastive Learning [[pdf]](https://arxiv.org/pdf/2110.15438.pdf)\n\n    Dongkuan Xu, Wei Cheng, Dongsheng Luo, Haifeng Chen, Xiang Zhang\n\n    *Graph contrastive learning*\n\n- Robustness of Graph Neural Networks at Scale [[pdf]](https://arxiv.org/pdf/2110.14038.pdf)\n\n    Simon Geisler, Tobias Schmidt, Hakan Şirin, Daniel Zügner, Aleksandar Bojchevski, Stephan Günnemann\n\n    *Robustness of GNNs*\n\n- Not All Low-Pass Filters are Robust in Graph Convolutional Networks [[pdf]](https://openreview.net/pdf?id=bDdfxLQITtu)\n\n    Heng Chang, Yu Rong, Tingyang Xu, Yatao Bian, Shiji Zhou, Xin Wang, Junzhou Huang, Wenwu Zhu\n\n    *Robustness of GNNs*\n\n-  Towards Open-World Feature Extrapolation- An Inductive Graph Learning Approach [[pdf]](https://arxiv.org/pdf/2110.04514.pdf)\n\n    Qitian Wu, Chenxiao Yang, Junchi Yan\n\n    *Application of GNNs: feature extrapolation*\n\n## KDD 2021\n- Adaptive Transfer Learning on Graph Neural Networks [[pdf]](https://arxiv.org/pdf/2107.08765.pdf)\n\n    Xueting Han, Zhenhuan Huang, Bang An, Jing Bai\n\n    *Transfer learning on GNNs*\n\n- Tail-GNN: Tail-Node Graph Neural Networks [[pdf]](https://dl.acm.org/doi/pdf/10.1145/3447548.3467276)\n\n    Zemin Liu, Trung-Kien Nguyen, Yuan Fang\n\n    *Long-tailed recognization on graph node degrees*\n\n- Zero-shot Node Classification with Decomposed Graph Prototype Network [[pdf]](https://arxiv.org/pdf/2106.08022.pdf)\n\n    Zheng Wang, Jialong Wang, Yuchen Guo, Zhiguo Gong\n\n    *Zero-shot Node Classification*\n\n- ImGAGN:Imbalanced Network Embedding via Generative Adversarial Graph Networks [[pdf]](https://arxiv.org/pdf/2106.02817.pdf) [[code]](https://github.com/Leo-Q-316/ImGAGN)\n\n    Liang Qu, Huaisheng Zhu, Ruiqi Zheng, Yuhui Shi, Hongzhi Yin\n\n    *Imbalanced Network Embedding*\n\n- ROD: Reception-aware Online Distillation for Sparse Graphs [[pdf]](https://arxiv.org/pdf/2107.11789.pdf)\n\n    Wentao Zhang, Yuezihan Jiang, Yang Li, et al.\n\n    *New architecture of GNNs*\n\n- When Comparing to Ground Truth is Wrong: On Evaluating GNN Explanation Methods [[pdf]](https://dl.acm.org/doi/abs/10.1145/3447548.3467283) [[code]](https://github.com/m30m/gnn-explainability)\n\n    Lukas Faber, Amin K. Moghaddam, Roger Wattenhofer\n\n    *Explanations of GNNs*\n\n## ICML 2021\n- Training Graph Neural Networks with 1000 Layers [[pdf]](https://arxiv.org/pdf/2106.07476.pdf) [[code]](https://www.deepgcns.org/arch/gnn1000)\n\n    Guohao Li, Matthias Müller, Bernard Ghanem, Vladlen Koltun\n\n    *Deeper GNNs*\n\n- GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training [[pdf]](https://arxiv.org/pdf/2009.03294.pdf)\n\n    Tianle Cai, Shengjie Luo, Keyulu Xu, Di He, Tie-Yan Liu, Liwei Wang\n\n    *Training mechanism*\n\n- Graph Contrastive Learning Automated [[pdf]](https://arxiv.org/pdf/2106.07594.pdf) [[code]](https://github.com/Shen-Lab/GraphCL_Automated)\n\n    Yuning You, Tianlong Chen, Yang Shen,  Zhangyang Wang\n\n    *Graph contrastive learning*\n\n- GNNAutoScale- Scalable and Expressive Graph Neural Networks via Historical Embeddings [[pdf]](https://arxiv.org/pdf/2106.05609.pdf) [[code]](https://github.com/rusty1s/pyg_autoscale)\n\n    Matthias Fey, Jan E. Lenssen, Frank Weichert, Jure Leskovec\n\n    *Large scale GNNs*\n\n- A Unified Lottery Ticket Hypothesis for Graph Neural Networks [[pdf]](https://arxiv.org/abs/2102.06790) [[code]](https://github.com/VITA-Group/Unified-LTH-GNN)\n\n    Tianlong Chen, Yongduo Sui, Xuxi Chen, Aston Zhang, Zhangyang Wang\n\n    *Sparse training on GNNs*\n\n- On Explainability of Graph Neural Networks via Subgraph Explorations [[pdf]](https://arxiv.org/pdf/2102.05152.pdf) [[code]](https://github.com/divelab/DIG)\n\n    Hao Yuan, Haiyang Yu, Jie Wang, Kang Li, Shuiwang Ji\n\n    *Explanations of GNNs*\n\n- Elastic Graph Neural Networks [[pdf]](https://arxiv.org/pdf/2107.06996.pdf) [[code]](https://github.com/lxiaorui/ElasticGNN)\n\n    Xiaorui Liu, Wei Jin, Yao Ma, Yaxin Li, Hua Liu, Yiqi Wang, Ming Yan, Jiliang Tang\n\n    *New architecture of GNNs*\n\n## WWW 2021\n- Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework [[pdf]](https://arxiv.org/pdf/2103.02885.pdf) [[code]](https://github.com/BUPT-GAMMA/CPF)\n    \n    Cheng Yang, Jiawei Liu, Chuan Shi\n\n    *Graph + knowledge distillation*\n\n- Graph Contrastive Learning with Adaptive Augmentation [[pdf]](https://arxiv.org/pdf/2010.14945.pdf) [[code]](https://github.com/CRIPAC-DIG/GCA)\n\n    Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, Liang Wang\n\n    *Graph contrastive learning*\n\n- HDMI: High-order Deep Multiplex Infomax [[pdf]](https://arxiv.org/pdf/2102.07810.pdf)\n\n    Baoyu Jing, Chanyoung Park, Hanghang Tong\n\n    *Multiplex graph representation learning*\n\n\n## ICLR 2021\n- HOW TO FIND YOUR FRIENDLY NEIGHBORHOOD: GRAPH ATTENTION DESIGN WITH SELF-SUPERVISION [[pdf]](https://openreview.net/pdf?id=Wi5KUNlqWty) [[code]](https://github.com/dongkwan-kim/SuperGAT)\n\n    Dongkwan Kim, Alice Oh\n\n    *Graph attention mechanism*\n\n- CopulaGNN: Towards Integrating Representational and Correlational Roles of Graphs in Graph Neural Networks [[pdf]](https://arxiv.org/pdf/2010.02089.pdf) [[code]](https://github.com/jiaqima/CopulaGNN)\n    \n    Jiaqi Ma, Bo Chang, Xuefei Zhang, Qiaozhu Mei\n\n    *Representational and correlational roles of graphs*\n\n- How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks [[pdf]](https://openreview.net/pdf?id=UH-cmocLJC)\n\n    Keyulu Xu, Mozhi Zhang, Jingling Li, Simon S. Du, Ken-ichi Kawarabayashi, Stefanie Jegelka\n\n    *Extrapolation*\n\n- On the Bottleneck of Graph Neural Networks and its Practical Implications [[pdf]](https://arxiv.org/pdf/2006.05205.pdf) [[code]](https://github.com/tech-srl/bottleneck/)\n    \n    Uri Alon, Eran Yahav\n\n    *over-squashing on GNNs*\n## NeurIPS 2020\n- Graph Random Neural Network for Semi-Supervised Learning on Graphs [[pdf]](https://arxiv.org/pdf/2005.11079.pdf) [[code]](https://github.com/THUDM/GRAND)\n\n    Wenzheng Feng, Jie Zhang, Yuxiao Dong, Yu Han, Huanbo Luan, Qian Xu, Qiang Yang, Evgeny Kharlamov, Jie Tang\n\n    *New architecture of GNNs*\n\n- Graph Meta Learning via Local Subgraphs [[pdf]](https://arxiv.org/pdf/2006.07889.pdf) [[code]](https://github.com/mims-harvard/G-Meta)\n\n    Kexin Huang, Marinka Zitnik\n\n    *Graph meta learning*\n\n- Subgraph Neural Networks [[pdf]](https://arxiv.org/pdf/2006.10538.pdf) [[code]](https://github.com/mims-harvard/SubGNN)\n\n    Emily Alsentzer, Samuel G. Finlayson, Michelle M. Li, Marinka Zitnik\n\n    *Subgraph GNNs*\n\n- Rethinking pooling in graph neural networks [[pdf]](https://arxiv.org/pdf/2010.11418.pdf) [[code]](https://github.com/AaltoPML/Rethinking-pooling-in-GNNs)\n\n    Diego Mesquita, Amauri H. Souza, Samuel Kaski\n\n    *Rethingking pooloing in GNNs*\n\n- Design Space for Graph Neural Networks [[pdf]](https://arxiv.org/pdf/2011.08843.pdf) [[code]](https://github.com/snap-stanford/graphgym)\n\n    Jiaxuan You, Rex Ying, Jure Leskovec\n\n    *Design space for GNNs*\n\n- Handling Missing Data with Graph Representation Learning [[pdf]](https://arxiv.org/pdf/2010.16418.pdf)\n\n    Jiaxuan You, Xiaobai Ma, Daisy Yi Ding, Mykel Kochenderfer, Jure Leskovec\n\n    *Matrix completion using GNNs*\n\n - Beyond Homophily in Graph Neural Networks- Current Limitations and Effective Designs [[pdf]](https://arxiv.org/pdf/2006.11468.pdf) [[code]](https://github.com/GemsLab/H2GCN)\n\n    Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, Danai Koutra\n\n    *Graph homophily*\n\n- GNNGuard: Defending Graph Neural Networks against Adversarial Attacks [[pdf]](https://arxiv.org/pdf/2006.08149.pdf) [[code]](https://github.com/mims-harvard/GNNGuard)\n\n    Xiang Zhang, Marinka Zitnik\n\n    *Graph robustness*\n\n- Graph Contrastive Learning with Augmentations [[pdf]](https://arxiv.org/pdf/2010.13902.pdf) [[code]](https://github.com/Shen-Lab/GraphCL)\n\n    Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, Yang Shen\n\n    *Graph contrastive learning*\n\n- Self-Supervised Graph Transformer on Large-Scale Molecular Data [[pdf]](https://arxiv.org/pdf/2007.02835.pdf)\n\n    Yu Rong, Yatao Bian, Tingyang Xu, Weiyang Xie, Ying Wei, Wenbing Huang, Junzhou Huang\n\n    *Graph transformer*\n\n- Scalable Graph Neural Networks via Bidirectional Propagation [[pdf]](https://arxiv.org/pdf/2010.15421.pdf) [[code]](https://github.com/chennnM/GBP)\n\n    Ming Chen, Zhewei Wei, Bolin Ding, Yaliang Li, Ye Yuan, Xiaoyong Du, Ji-Rong Wen\n\n    *Large scale GNNs*\n\n\n## KDD 2020\n- AM-GCN: Adaptive Multi-channel Graph Convolutional Networks [[pdf]](https://arxiv.org/pdf/2007.02265.pdf) [[code]](https://github.com/zhumeiqiBUPT/AM-GCN)\n\n    Xiao Wang, Meiqi Zhu, Deyu Bo, Peng Cui, Chuan Shi, Jian Pei\n\n    *New architecture of GNNs*\n\n- Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks [[pdf]](https://arxiv.org/pdf/2005.11650.pdf) [[code]](https://github.com/nnzhan/MTGNN)\n\n    Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Xiaojun Chang, Chengqi Zhang\n\n    *Graph + time series*\n\n- GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training [[pdf]](https://arxiv.org/pdf/2006.09963.pdf) [[code]](https://github.com/THUDM/GCC)\n\n    Jiezhong Qiu, Qibin Chen, Yuxiao Dong, Jing Zhang, Hongxia Yang, Ming Ding, Kuansan Wang, Jie Tang\n\n    *Grapg contrastive learning*\n\n- Towards Deeper Graph Neural Networks [[pdf]](https://arxiv.org/pdf/2007.09296.pdf) [[code]](https://github.com/divelab/DeeperGNN)\n\n    Meng Liu, Hongyang Gao, Shuiwang Ji\n\n    *Deeper GNNs*\n\n- TinyGNN: Learning Efficient Graph Neural Networks [[pdf]](https://dl.acm.org/doi/10.1145/3394486.3403236)\n\n    Bencheng Yan, Chaokun Wang, Gaoyang Guo, Yunkai Lou\n\n    *Large scale GNNs*\n\n- XGNN: Towards Model-Level Explanations of Graph Neural Networks [[pdf]](https://arxiv.org/pdf/2006.02587.pdf)\n\n    Hao Yuan, Jiliang Tang, Xia Hu, Shuiwang Ji\n \n    *Explanations of GNNs*\n\n\n## AAAI 2021\n- Beyond Low-frequency Information in Graph Convolutional Networks [[pdf]](https://arxiv.org/pdf/2101.00797.pdf) [[code]](https://github.com/bdy9527/FAGCN)\n\n    Deyu Bo, Xiao Wang, Chuan Shi, Huawei Shen\n\n    *New architecture of GNNs*\n\n- Data Augmentation for Graph Neural Networks [[pdf]](https://arxiv.org/pdf/2006.06830.pdf) [[code]](https://github.com/zhao-tong/GAug)\n\n    Tong Zhao, Yozen Liu, Leonardo Neves, Oliver Woodford, Meng Jiang, Neil Shah\n\n    *Graph data augmentation*\n\n- GraphMix: Improved Training of GNNs for Semi-Supervised Learning [[pdf]](https://arxiv.org/pdf/1909.11715.pdf) [[code]](https://github.com/vikasverma1077/GraphMix)\n\n    Vikas Verma, Meng Qu, Kenji Kawaguchi, Alex Lamb, Yoshua Bengio, Juho Kannala, Jian Tang\n\n    *New architecture of GNNs*\n\n- Identity-aware Graph Neural networks [[pdf]](https://arxiv.org/pdf/2101.10320.pdf) \n\n    Jiaxuan You, Jonathan Gomes-Selman, Rex Ying, Jure Leskovec\n\n    *New architecture of GNNs*\n\n- Learning to Pre-train Graph Neural Networks [[pdf]](http://www.shichuan.org/doc/101.pdf) [[code]](https://github.com/rootlu/L2P-GNN)\n\n    Yuanfu Lu, Xunqiang Jiang, Yuan Fang, Chuan Shi\n\n    *Pre-training of GNNs*\n\n## ICML 2020\n- Contrastive Multi-View Representation Learning on Graphs [[pdf]](https://arxiv.org/pdf/2006.05582.pdf)\n\n    Kaveh Hassani, Amir Hosein Khasahmadi\n\n    *Graph contrastive learning*\n\n- Graph Structure of Neural Networks [[pdf]](https://arxiv.org/pdf/2007.06559.pdf) [[code]](https://github.com/facebookresearch/graph2nn)\n\n    Jiaxuan You, Jure Leskovec, Kaiming He, Saining Xie\n\n    *Graph structure*\n\n- Robust Graph Representation Learning via Neural Sparsification [[pdf]](http://proceedings.mlr.press/v119/zheng20d/zheng20d.pdf)\n\n    Cheng Zheng, Bo Zong, Wei Cheng, et al. \n\n    *Graph sparsification*\n\n- Simple and Deep Graph Convolutional Networks [[pdf]](https://arxiv.org/pdf/2007.02133.pdf) [[code]](https://github.com/chennnM/GCNII)\n\n    Ming Chen, Zhewei Wei, Zengfeng Huang, Bolin Ding, Yaliang Li\n\n    *New architecture of GNNs*\n\n- When Does Self-Supervision Help Graph Convolutional Networks? [[pdf]](https://arxiv.org/pdf/2006.09136.pdf) [[code]](https://github.com/Shen-Lab/SS-GCNs)\n\n    Yuning You, Tianlong Chen, Zhangyang Wang, Yang Shen\n\n    *Graph self-supervision learning*\n\n\n## ICLR 2020\n- DropEdge: Towards Deep Graph Convolutional Networks on Node Classification [[pdf]](https://arxiv.org/pdf/1907.10903.pdf) [[code]](https://github.com/DropEdge/DropEdge)\n\n    Yu Rong, Wenbing Huang, Tingyang Xu, Junzhou Huang\n\n    *New architecture of GNNs*\n\n- Geom-GCN: Geometric Graph Convolutional Networks [[pdf]](https://arxiv.org/pdf/2002.05287.pdf) [[code]](https://github.com/graphdml-uiuc-jlu/geom-gcn)\n\n    Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, Bo Yang\n\n    *New architecture of GNNs*\n\n- GraphSAINT: Graph Sampling Based Inductive Learning Method [[pdf]](https://arxiv.org/pdf/1907.04931.pdf) [[code]](https://github.com/GraphSAINT/GraphSAINT)\n\n    Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, Viktor Prasanna\n\n    *Large scale GNNs*\n\n- PairNorm: Tackling Oversmoothing in GNNs [[pdf]](https://arxiv.org/pdf/1909.12223.pdf) [[code]](https://github.com/LingxiaoShawn/PairNorm)\n\n    Lingxiao Zhao, Leman Akoglu\n\n    *Deeper GNNs*\n\n- Strategies for Pre-training Graph Neural Networks [[pdf]](https://arxiv.org/pdf/1905.12265.pdf) [[code]](https://github.com/snap-stanford/pretrain-gnns)\n\n    Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, Jure Leskovec\n\n    *Graph pre-training*\n\n- WHAT GRAPH NEURAL NETWORKS CANNOT LEARN: DEPTH VS WIDTH [[pdf]](https://openreview.net/pdf?id=B1l2bp4YwS)\n\n    Andreas Loukas\n\n    *Expressive power of GNNs*\n\n- Neural Execution of Graph Algorithms [[pdf]](https://openreview.net/pdf?id=SkgKO0EtvS)\n\n    Petar Veličković, Rex Ying, Matilde Padovano, Raia Hadsell, Charles Blundell\n\n    *Algorithmic reasoning*\n\n\n- What Can Neural Networks Reason About?[[pdf]](https://openreview.net/forum?id=rJxbJeHFPS)\n\n    Keyulu Xu, Jingling Li, Mozhi Zhang, Simon S. Du, Ken-ichi Kawarabayashi, Stefanie Jegelka\n\n    *Algorithmic reasoning*\n\n## NeurIPS 2019\n- GNNExplainer: Generating Explanations for Graph Neural Networks [[pdf]](https://arxiv.org/pdf/1903.03894.pdf) [[code]](https://github.com/RexYing/gnn-model-explainer)\n\n    Rex Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, Jure Leskovec\n\n    *Explanations of GNNs*\n\n- Understanding Attention and Generalization in Graph Neural Networks [[pdf]](https://arxiv.org/pdf/1905.02850.pdf) [[code]](https://github.com/bknyaz/graph_attention_pool)\n\n    Boris Knyazev, Graham W. Taylor, Mohamed R. Amer\n\n    *Understanding attention in GNNs*\n \n\n## Some Must-Read Papers\n- Collective dynamics of 'small-world' networks [[pdf]](https://www.nature.com/articles/30918)\n\n    Watts, Duncan J and Strogatz, Steven H\n\n    Nature 1998\n\n    *'Small-world phenomena'*\n\n- Network motifs: simple building blocks of complex networks [[pdf]](https://science.sciencemag.org/content/298/5594/824)\n\n    R. Milo, S. Shen-Orr, S. Itzkovitz, N. Kashtan, D. Chklovskii, U. Alon\n\n    Science 2002 \n\n    *Network motifs*\n\n- Rolx: structural role extraction \\\u0026 mining in large graphs [[pdf]](https://dl.acm.org/doi/pdf/10.1145/2339530.2339723)\n\n    Keith Henderson, Brian Gallagher, Tina Eliassi-Rad, et al.\n\n    KDD 2012\n\n    *Structural rele*\n\n- Birds of a feather: Homophily in social networks [[pdf]](https://www.annualreviews.org/doi/pdf/10.1146/annurev.soc.27.1.415)\n\n    McPherson, Miller and Smith-Lovin, Lynn and Cook, James M\n\n    Annual review of sociology 2001\n\n    *Homophily phenomena*\n\n- Network embedding as matrix factorization: Unifying deepwalk, line, pte, and node2vec [[pdf]](https://arxiv.org/abs/1710.02971.pdf) [[code]](https://github.com/xptree/NetMF)\n\n    Qiu, Jiezhong and Dong, Yuxiao and Ma, Hao and Li, Jian and Wang, Kuansan and Tang, Jie\n\n    WSDM 2018\n\n    *Unified framework for network embedding*\n\n\n# Talks\n- Graph Neural Networks with Learnable Structural and Positional Representation [[video]](https://www.youtube.com/watch?v=hADjUl4ymoQ)\n\n    Xavier Bresson 2021\n\n- Graph Representation Learning:Foundations, Methods, Applications and Systems [[pdf]](https://kdd2021graph.github.io)\n\n    KDD 2021 Graph tutorial\n\n- Graph Neural Networks: Algorithms and Applications [[pdf]](https://drive.google.com/file/d/1ULelq5bs7bU1iQLhQnapVSQO6fJnitSJ/view)\n\n    Jian Tang 2021\n\n- Graph Representation Learning for Drug Discovery [[pdf]](https://drive.google.com/file/d/19e0scMh4Fxzsbq6a8Z9idsYcsnLAgYAx/view)\n\n    Jian Tang 2021\n\n- Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs [[pdf]](https://www.jiongzhu.net/assets/files/F20-Jiong-H2GCN-NeurIPS-Talk.pdf)\n\n    Jiong Zhu 2021\n\n- Theoretical Foundations of Graph Neural Networks [[pdf]](https://petar-v.com/talks/GNN-Wednesday.pdf) [[video]](https://www.youtube.com/watch?v=uF53xsT7mjc)\n\n    Petar Veličković 2021\n\n- Expressive Power of Graph Neural Networks [[video]](https://www.bilibili.com/video/BV1Dz4y1Q7d4?from=search\u0026seid=12042387670249475077)\n\n    Huawei Shen 2020\n\n- Graph Representation Learning for Algorithmic Reasoning [[pdf]](https://petar-v.com/talks/Algo-WWW.pdf) [[video]](https://www.youtube.com/watch?v=IPQ6CPoluok)\n\n    Petar Veličković 2020\n\n# Blog posts\n- Graph Neural Networks as Neural Diffusion PDEs [[URL]](https://towardsdatascience.com/graph-neural-networks-as-neural-diffusion-pdes-8571b8c0c774)\n\n    Michael Bronstein 2022\n\n- Graph Contrastive learning [[URL]](https://sxkdz.github.io/research/GraphCL/)\n\n    Yanqiao Zhu 2021\n\n- Temporal Graph Networks [[URL]](https://towardsdatascience.com/temporal-graph-networks-ab8f327f2efe)\n\n    Michael Bronstein 2020\n\n- Graph Diffusion Convolution [[URL]](https://msrmblog.github.io/graph-diffusion-convolution/)\n\n    Johannes Klicpera 2020\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzlpure%2Fawesome-graph-representation-learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fzlpure%2Fawesome-graph-representation-learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzlpure%2Fawesome-graph-representation-learning/lists"}