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Awesome Deep Graph Representation Learning

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A curated list for awesome deep graph representation learning resources. Inspired by awesome-deep-learning-papers, awesome-deep-vision, awesome-architecture-search, awesome-self-supervised-learning-for-graphs, and awesome-deep-gnn.

Background

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.      - - William L. Hamilton

Graph 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.

Contributing

You are welcome to contribute this repo by contracting me or adding pull request.

Markdown formart:

Paper Name [[pdf]](link) [[code]](link)

Author 1, Author 2, Author 3. 

Conference Year

*Taxonomy* (No more than 5 words)

Table of Contents

Papers

Surveys

  • Graph Representation Learning [pdf]

    William L. Hamilton

    Book

    Classical survey

  • Networks, Crowds, and Markets - Reasoning About a Highly Connected World [pdf]

    D Easley, J Kleinberg

    Book

    Basic concepts on Networks

  • Network Science [pdf]

    Albert-László Barabási

    Book

    Basic concepts on Networks

  • Relational inductive biases, deep learning, and graph networks [pdf]

    Battaglia, Peter W and Hamrick, Jessica B, et al.

    Arxiv 2018

    Relational inductive biases on graphs

  • A comprehensive survey on graph neural networks [pdf]

    Zonghan Wu, Shirui Pan, Chen, Guodong Long, Chengqi Zhang, Philip, S Yu

    IEEE 2020

    Survey

  • Self-Supervised Learning of Graph Neural Networks: A Unified Review [pdf]

    Yaochen Xie, Zhao Xu, Jingtun Zhang, Zhengyang Wang, Shuiwang Ji

    Arxiv 2021

    Self-supervised learning

  • Combinatorial optimization and reasoning with graph neural networks [pdf]

    Quentin Cappart, Didier Chételat, Elias Khalil, Andrea Lodi, Christopher Morris, Petar Veličković

    IJCAI 2021

    Survey on GNNs for combinatorial optimization and algorithmic reasoning

ICML 2022

  • 3D Infomax improves GNNs for Molecular Property Prediction [pdf] [code]

    Hannes Stärk, Dominique Beaini, Gabriele Corso, et al.

    Molecular property prediction on 3D molecule geometry

  • EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction [pdf] [code]

    Hannes Stärk, Octavian-Eugen Ganea, et al.

    Drug-protein binding prediction

  • G-Mixup: Graph Data Augmentation for Graph Classification [pdf] [code]

    Xiaotian Han, Zhimeng Jiang, Ninghao Liu, Xia Hu

    Mixup on graphs

ICLR 2022

  • Context-Aware Sparse Deep Coordination Graphs [pdf] [code]

    Tonghan Wang, Liang Zeng, et al.

    Coordination graphs on multi-agent RL

  • On the Unreasonable Effectiveness of Feature propagation in Learning on Graphs with Missing Node Features [pdf]

    Emanuele Rossi, Henry Kenlay, et al.

    Feature propagation on graphs

  • Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods [pdf] [code]

    Wenqing Zheng, Edward W Huang, et al.

    Imbalanced learning on graphs

  • Equivariant Graph Mechanics Networks with Constraints [pdf] [code]

    Wenbing Huang, Jiaqi Han, et al.

    AI for science using GNNs

  • Discovering Invariant Rationales for Graph Neural Networks [pdf] [code]

    Ying-Xin Wu, Xiang Wang, An Zhang, Xiangnan He, Tat-Seng Chua

    Causal inference on graphs

  • Is Homophily a Necessity for Graph Neural Networks? [pdf]

    Yao Ma, Xiaorui Liu, Neil Shah, Jiliang Tang

    Homophily property on GNNs

  • Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design [pdf]

    Wengong Jin, Jeremy Wohlwend, Regina Barzilay, Tommi Jaakkola

    AI for drugs using GNNs

  • Graph-Guided Network for Irregularly Sampled Multivariate Time Series [pdf] [code]

    Xiang Zhang, Marko Zeman, Theodoros Tsiligkaridis, Marinka Zitnik

    Temporal-spatial data using GNNs

  • Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and Practical Solutions [pdf] [code]

    Leslie O'Bray, Max Horn, Bastian Rieck, Karsten Borgwardt

    Evaluation of graph generation

  • Context-Aware Sparse Deep Coordination Graphs [pdf] [code]

    Tonghan Wang, Liang Zeng, Weijun Dong, Qianlan Yang, Yang Yu, Chongjie Zhang

    Coordination graphs

WWW 2022

  • Towards Unsupervised Deep Graph Structure Learning [pdf] [code]

    Yixin Liu, Yu Zheng, Daokun Zhang, Hongxu Chen, Hao Peng, Shirui Pan

    Graph structure learning

  • ClusterSCL: Cluster-Aware Supervised Contrastive Learning on Graphs [pdf] [code]

    Yanling Wang, Jing Zhang, et al.

    Graph contrastive learning

  • ALLIE: Active Learning on Large-scale Imbalanced Graphs [pdf]

    Limeng Cui, Xianfeng Tang, et al.

    Active learning & Imbalanced learning

  • PaSca: A Graph Neural Architecture Search System under the Scalable Paradigm [pdf] (Best candidiate paper)

    Wentao Zhang, Yu Shen, et al.

    Neural architecture search on graphs

NeurIPS 2021

  • Multi-view Contrastive Graph Clustering [pdf] [code]

    Erlin Pan, Zhao Kang

    Graph clustering

  • Subgraph Federated Learning with Missing Neighbor Generation [pdf]

    Ke Zhang, Carl Yang, Xiaoxiao Li, Lichao Sun, Siu Ming Yiu

    Federated learning on graphs

  • Edge Representation Learning with Hypergraphs [pdf] [code]

    Jaehyeong Jo, Jinheon Baek, Seul Lee, et al.

    Edge representation learning on graphs

  • Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration [pdf]

    Xiao Wang, Hongrui Liu, Chuan Shi, Cheng Yang

    Confidence calibration of GNNs

  • InfoGCL: Information-Aware Graph Contrastive Learning [pdf]

    Dongkuan Xu, Wei Cheng, Dongsheng Luo, Haifeng Chen, Xiang Zhang

    Graph contrastive learning

  • Robustness of Graph Neural Networks at Scale [pdf]

    Simon Geisler, Tobias Schmidt, Hakan Şirin, Daniel Zügner, Aleksandar Bojchevski, Stephan Günnemann

    Robustness of GNNs

  • Not All Low-Pass Filters are Robust in Graph Convolutional Networks [pdf]

    Heng Chang, Yu Rong, Tingyang Xu, Yatao Bian, Shiji Zhou, Xin Wang, Junzhou Huang, Wenwu Zhu

    Robustness of GNNs

  • Towards Open-World Feature Extrapolation- An Inductive Graph Learning Approach [pdf]

    Qitian Wu, Chenxiao Yang, Junchi Yan

    Application of GNNs: feature extrapolation

KDD 2021

  • Adaptive Transfer Learning on Graph Neural Networks [pdf]

    Xueting Han, Zhenhuan Huang, Bang An, Jing Bai

    Transfer learning on GNNs

  • Tail-GNN: Tail-Node Graph Neural Networks [pdf]

    Zemin Liu, Trung-Kien Nguyen, Yuan Fang

    Long-tailed recognization on graph node degrees

  • Zero-shot Node Classification with Decomposed Graph Prototype Network [pdf]

    Zheng Wang, Jialong Wang, Yuchen Guo, Zhiguo Gong

    Zero-shot Node Classification

  • ImGAGN:Imbalanced Network Embedding via Generative Adversarial Graph Networks [pdf] [code]

    Liang Qu, Huaisheng Zhu, Ruiqi Zheng, Yuhui Shi, Hongzhi Yin

    Imbalanced Network Embedding

  • ROD: Reception-aware Online Distillation for Sparse Graphs [pdf]

    Wentao Zhang, Yuezihan Jiang, Yang Li, et al.

    New architecture of GNNs

  • When Comparing to Ground Truth is Wrong: On Evaluating GNN Explanation Methods [pdf] [code]

    Lukas Faber, Amin K. Moghaddam, Roger Wattenhofer

    Explanations of GNNs

ICML 2021

  • Training Graph Neural Networks with 1000 Layers [pdf] [code]

    Guohao Li, Matthias Müller, Bernard Ghanem, Vladlen Koltun

    Deeper GNNs

  • GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training [pdf]

    Tianle Cai, Shengjie Luo, Keyulu Xu, Di He, Tie-Yan Liu, Liwei Wang

    Training mechanism

  • Graph Contrastive Learning Automated [pdf] [code]

    Yuning You, Tianlong Chen, Yang Shen, Zhangyang Wang

    Graph contrastive learning

  • GNNAutoScale- Scalable and Expressive Graph Neural Networks via Historical Embeddings [pdf] [code]

    Matthias Fey, Jan E. Lenssen, Frank Weichert, Jure Leskovec

    Large scale GNNs

  • A Unified Lottery Ticket Hypothesis for Graph Neural Networks [pdf] [code]

    Tianlong Chen, Yongduo Sui, Xuxi Chen, Aston Zhang, Zhangyang Wang

    Sparse training on GNNs

  • On Explainability of Graph Neural Networks via Subgraph Explorations [pdf] [code]

    Hao Yuan, Haiyang Yu, Jie Wang, Kang Li, Shuiwang Ji

    Explanations of GNNs

  • Elastic Graph Neural Networks [pdf] [code]

    Xiaorui Liu, Wei Jin, Yao Ma, Yaxin Li, Hua Liu, Yiqi Wang, Ming Yan, Jiliang Tang

    New architecture of GNNs

WWW 2021

  • Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework [pdf] [code]

    Cheng Yang, Jiawei Liu, Chuan Shi

    Graph + knowledge distillation

  • Graph Contrastive Learning with Adaptive Augmentation [pdf] [code]

    Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, Liang Wang

    Graph contrastive learning

  • HDMI: High-order Deep Multiplex Infomax [pdf]

    Baoyu Jing, Chanyoung Park, Hanghang Tong

    Multiplex graph representation learning

ICLR 2021

  • HOW TO FIND YOUR FRIENDLY NEIGHBORHOOD: GRAPH ATTENTION DESIGN WITH SELF-SUPERVISION [pdf] [code]

    Dongkwan Kim, Alice Oh

    Graph attention mechanism

  • CopulaGNN: Towards Integrating Representational and Correlational Roles of Graphs in Graph Neural Networks [pdf] [code]

    Jiaqi Ma, Bo Chang, Xuefei Zhang, Qiaozhu Mei

    Representational and correlational roles of graphs

  • How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks [pdf]

    Keyulu Xu, Mozhi Zhang, Jingling Li, Simon S. Du, Ken-ichi Kawarabayashi, Stefanie Jegelka

    Extrapolation

  • On the Bottleneck of Graph Neural Networks and its Practical Implications [pdf] [code]

    Uri Alon, Eran Yahav

    over-squashing on GNNs

NeurIPS 2020

  • Graph Random Neural Network for Semi-Supervised Learning on Graphs [pdf] [code]

    Wenzheng Feng, Jie Zhang, Yuxiao Dong, Yu Han, Huanbo Luan, Qian Xu, Qiang Yang, Evgeny Kharlamov, Jie Tang

    New architecture of GNNs

  • Graph Meta Learning via Local Subgraphs [pdf] [code]

    Kexin Huang, Marinka Zitnik

    Graph meta learning

  • Subgraph Neural Networks [pdf] [code]

    Emily Alsentzer, Samuel G. Finlayson, Michelle M. Li, Marinka Zitnik

    Subgraph GNNs

  • Rethinking pooling in graph neural networks [pdf] [code]

    Diego Mesquita, Amauri H. Souza, Samuel Kaski

    Rethingking pooloing in GNNs

  • Design Space for Graph Neural Networks [pdf] [code]

    Jiaxuan You, Rex Ying, Jure Leskovec

    Design space for GNNs

  • Handling Missing Data with Graph Representation Learning [pdf]

    Jiaxuan You, Xiaobai Ma, Daisy Yi Ding, Mykel Kochenderfer, Jure Leskovec

    Matrix completion using GNNs

  • Beyond Homophily in Graph Neural Networks- Current Limitations and Effective Designs [pdf] [code]

    Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, Danai Koutra

    Graph homophily

  • GNNGuard: Defending Graph Neural Networks against Adversarial Attacks [pdf] [code]

    Xiang Zhang, Marinka Zitnik

    Graph robustness

  • Graph Contrastive Learning with Augmentations [pdf] [code]

    Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, Yang Shen

    Graph contrastive learning

  • Self-Supervised Graph Transformer on Large-Scale Molecular Data [pdf]

    Yu Rong, Yatao Bian, Tingyang Xu, Weiyang Xie, Ying Wei, Wenbing Huang, Junzhou Huang

    Graph transformer

  • Scalable Graph Neural Networks via Bidirectional Propagation [pdf] [code]

    Ming Chen, Zhewei Wei, Bolin Ding, Yaliang Li, Ye Yuan, Xiaoyong Du, Ji-Rong Wen

    Large scale GNNs

KDD 2020

  • AM-GCN: Adaptive Multi-channel Graph Convolutional Networks [pdf] [code]

    Xiao Wang, Meiqi Zhu, Deyu Bo, Peng Cui, Chuan Shi, Jian Pei

    New architecture of GNNs

  • Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks [pdf] [code]

    Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Xiaojun Chang, Chengqi Zhang

    Graph + time series

  • GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training [pdf] [code]

    Jiezhong Qiu, Qibin Chen, Yuxiao Dong, Jing Zhang, Hongxia Yang, Ming Ding, Kuansan Wang, Jie Tang

    Grapg contrastive learning

  • Towards Deeper Graph Neural Networks [pdf] [code]

    Meng Liu, Hongyang Gao, Shuiwang Ji

    Deeper GNNs

  • TinyGNN: Learning Efficient Graph Neural Networks [pdf]

    Bencheng Yan, Chaokun Wang, Gaoyang Guo, Yunkai Lou

    Large scale GNNs

  • XGNN: Towards Model-Level Explanations of Graph Neural Networks [pdf]

    Hao Yuan, Jiliang Tang, Xia Hu, Shuiwang Ji

    Explanations of GNNs

AAAI 2021

  • Beyond Low-frequency Information in Graph Convolutional Networks [pdf] [code]

    Deyu Bo, Xiao Wang, Chuan Shi, Huawei Shen

    New architecture of GNNs

  • Data Augmentation for Graph Neural Networks [pdf] [code]

    Tong Zhao, Yozen Liu, Leonardo Neves, Oliver Woodford, Meng Jiang, Neil Shah

    Graph data augmentation

  • GraphMix: Improved Training of GNNs for Semi-Supervised Learning [pdf] [code]

    Vikas Verma, Meng Qu, Kenji Kawaguchi, Alex Lamb, Yoshua Bengio, Juho Kannala, Jian Tang

    New architecture of GNNs

  • Identity-aware Graph Neural networks [pdf]

    Jiaxuan You, Jonathan Gomes-Selman, Rex Ying, Jure Leskovec

    New architecture of GNNs

  • Learning to Pre-train Graph Neural Networks [pdf] [code]

    Yuanfu Lu, Xunqiang Jiang, Yuan Fang, Chuan Shi

    Pre-training of GNNs

ICML 2020

  • Contrastive Multi-View Representation Learning on Graphs [pdf]

    Kaveh Hassani, Amir Hosein Khasahmadi

    Graph contrastive learning

  • Graph Structure of Neural Networks [pdf] [code]

    Jiaxuan You, Jure Leskovec, Kaiming He, Saining Xie

    Graph structure

  • Robust Graph Representation Learning via Neural Sparsification [pdf]

    Cheng Zheng, Bo Zong, Wei Cheng, et al.

    Graph sparsification

  • Simple and Deep Graph Convolutional Networks [pdf] [code]

    Ming Chen, Zhewei Wei, Zengfeng Huang, Bolin Ding, Yaliang Li

    New architecture of GNNs

  • When Does Self-Supervision Help Graph Convolutional Networks? [pdf] [code]

    Yuning You, Tianlong Chen, Zhangyang Wang, Yang Shen

    Graph self-supervision learning

ICLR 2020

  • DropEdge: Towards Deep Graph Convolutional Networks on Node Classification [pdf] [code]

    Yu Rong, Wenbing Huang, Tingyang Xu, Junzhou Huang

    New architecture of GNNs

  • Geom-GCN: Geometric Graph Convolutional Networks [pdf] [code]

    Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, Bo Yang

    New architecture of GNNs

  • GraphSAINT: Graph Sampling Based Inductive Learning Method [pdf] [code]

    Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, Viktor Prasanna

    Large scale GNNs

  • PairNorm: Tackling Oversmoothing in GNNs [pdf] [code]

    Lingxiao Zhao, Leman Akoglu

    Deeper GNNs

  • Strategies for Pre-training Graph Neural Networks [pdf] [code]

    Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, Jure Leskovec

    Graph pre-training

  • WHAT GRAPH NEURAL NETWORKS CANNOT LEARN: DEPTH VS WIDTH [pdf]

    Andreas Loukas

    Expressive power of GNNs

  • Neural Execution of Graph Algorithms [pdf]

    Petar Veličković, Rex Ying, Matilde Padovano, Raia Hadsell, Charles Blundell

    Algorithmic reasoning

  • What Can Neural Networks Reason About?[pdf]

    Keyulu Xu, Jingling Li, Mozhi Zhang, Simon S. Du, Ken-ichi Kawarabayashi, Stefanie Jegelka

    Algorithmic reasoning

NeurIPS 2019

  • GNNExplainer: Generating Explanations for Graph Neural Networks [pdf] [code]

    Rex Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, Jure Leskovec

    Explanations of GNNs

  • Understanding Attention and Generalization in Graph Neural Networks [pdf] [code]

    Boris Knyazev, Graham W. Taylor, Mohamed R. Amer

    Understanding attention in GNNs

Some Must-Read Papers

  • Collective dynamics of 'small-world' networks [pdf]

    Watts, Duncan J and Strogatz, Steven H

    Nature 1998

    'Small-world phenomena'

  • Network motifs: simple building blocks of complex networks [pdf]

    R. Milo, S. Shen-Orr, S. Itzkovitz, N. Kashtan, D. Chklovskii, U. Alon

    Science 2002

    Network motifs

  • Rolx: structural role extraction & mining in large graphs [pdf]

    Keith Henderson, Brian Gallagher, Tina Eliassi-Rad, et al.

    KDD 2012

    Structural rele

  • Birds of a feather: Homophily in social networks [pdf]

    McPherson, Miller and Smith-Lovin, Lynn and Cook, James M

    Annual review of sociology 2001

    Homophily phenomena

  • Network embedding as matrix factorization: Unifying deepwalk, line, pte, and node2vec [pdf] [code]

    Qiu, Jiezhong and Dong, Yuxiao and Ma, Hao and Li, Jian and Wang, Kuansan and Tang, Jie

    WSDM 2018

    Unified framework for network embedding

Talks

  • Graph Neural Networks with Learnable Structural and Positional Representation [video]

    Xavier Bresson 2021

  • Graph Representation Learning:Foundations, Methods, Applications and Systems [pdf]

    KDD 2021 Graph tutorial

  • Graph Neural Networks: Algorithms and Applications [pdf]

    Jian Tang 2021

  • Graph Representation Learning for Drug Discovery [pdf]

    Jian Tang 2021

  • Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs [pdf]

    Jiong Zhu 2021

  • Theoretical Foundations of Graph Neural Networks [pdf] [video]

    Petar Veličković 2021

  • Expressive Power of Graph Neural Networks [video]

    Huawei Shen 2020

  • Graph Representation Learning for Algorithmic Reasoning [pdf] [video]

    Petar Veličković 2020

Blog posts

  • Graph Neural Networks as Neural Diffusion PDEs [URL]

    Michael Bronstein 2022

  • Graph Contrastive learning [URL]

    Yanqiao Zhu 2021

  • Temporal Graph Networks [URL]

    Michael Bronstein 2020

  • Graph Diffusion Convolution [URL]

    Johannes Klicpera 2020

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