图神经网络（GNN）上自监督学习的论文列表，内容丰富

本文来源「深度学习与图网络」，版权属于原作者

下面是关于图神经网络（GNN）上自监督学习的论文列表, 内容丰富，更新及时，欢迎收藏。

自监督学习主要分为三类：生成式、对比学习、Adversarial(Generative-Contrastive)。目前，个人认为大部分Graph研究的目光都集中在Contrstive Learning上。个人拙见，原因可能与图学习的任务有关，图学习的任务主要集中在分类上(节点分类、图分类)，对比学习天然会比生成学习更适用于分类任务，所以或许当生成满足某种性质的随机图任务成为主流之后，生成式模型就会成为主流。而对抗式(Adversarial)的学习，则会在生成式学习、对比式学习都达到瓶颈时，得到更好的发展。目前，在图领域，并未看到Adversarial Learning有惊人表现的文章。

知乎-十四楼的残魂

2021年论文列表

1. [IJCNN 2021] Node Embedding using Mutual Information and Self-Supervision based Bi-level Aggregation[paper]

2. [arXiv 2021] Hop-Count Based Self-Supervised Anomaly Detection on Attributed Networks[paper]

3. [arXiv 2021] Representation Learning for Networks in Biology and Medicine: Advancements, Challenges, and Opportunities[paper]

4. [arXiv 2021] Graph Representation Learning by Ensemble Aggregating Subgraphs via Mutual Information Maximization[paper]

5. [arXiv 2021] Drug Target Prediction Using Graph Representation Learning via Substructures Contrast[paper]

6. [arXiv 2021] Self-supervised Auxiliary Learning for Graph Neural Networks via Meta-Learning[paper]

7. [arXiv 2021] Graph Self-Supervised Learning: A Survey[paper]

8. [arXiv 2021] Towards Robust Graph Contrastive Learning[paper]

9. [arXiv 2021] Pre-Training on Dynamic Graph Neural Networks[paper]

10. [arXiv 2021] Self-Supervised Learning of Graph Neural Networks: A Unified Review[paper]

11. [WWW 2021 Workshop] Iterative Graph Self-Distillation[paper]

12. [WWW 2021] Graph Contrastive Learning with Adaptive Augmentation[paper][code]

13. [WWW 2021] SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanism[paper][code]

14. [Arxiv 2021] Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation[paper][code]

15. [ICLR 2021] How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision[paper][code]

16. [WSDM 2021] Pre-Training Graph Neural Networks for Cold-Start Users and Items Representation[paper][code]

2020年论文列表

1. [Arxiv 2020] COAD: Contrastive Pre-training with Adversarial Fine-tuning for Zero-shot Expert Linking[paper][code]

2. [Arxiv 2020] Distance-wise Graph Contrastive Learning[paper]

3. [Openreview 2020] Motif-Driven Contrastive Learning of Graph Representations[paper]

4. [Openreview 2020] SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks[paper]

5. [Openreview 2020] TopoTER: Unsupervised Learning of Topology Transformation Equivariant Representations[paper]

6. [Openreview 2020] Graph-Based Neural Network Models with Multiple Self-Supervised Auxiliary Tasks[paper]

7. [Openreview 2020] Self-supervised Graph-level Representation Learning with Local and Global Structure[paper]

8. [Openreview 2020] Transfer Learning of Graph Neural Networks with Ego-graph Information Maximization[paper]

9. [NeurIPS 2020] Self-Supervised Graph Transformer on Large-Scale Molecular Data[paper]

10. [NeurIPS 2020] Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs[paper][code]

11. [NeurIPS 2020] Graph Contrastive Learning with Augmentations[paper][code]

12. [Arxiv 2020] Self-supervised Learning on Graphs: Deep Insights and New Direction.[paper][code]

13. [Arxiv 2020] Deep Graph Contrastive Representation Learning[paper]

14. [ICML 2020] When Does Self-Supervision Help Graph Convolutional Networks?[paper][code]

15. [ICML 2020] Graph-based, Self-Supervised Program Repair from Diagnostic Feedback.[paper]

16. [ICML 2020] Contrastive Multi-View Representation Learning on Graphs.[paper][code]

17. [ICML 2020 Workshop] Self-supervised edge features for improved Graph Neural Network training.[paper]

18. [Arxiv 2020] Self-supervised Training of Graph Convolutional Networks.[paper]

19. [Arxiv 2020] Self-Supervised Graph Representation Learning via Global Context Prediction.[paper]

20. [KDD 2020] GPT-GNN: Generative Pre-Training of Graph Neural Networks.[pdf][code]

21. [KDD 2020] GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training.[pdf][code]

22. [Arxiv 2020] Graph-Bert: Only Attention is Needed for Learning Graph Representations.[paper][code]

23. [ICLR 2020] InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization.[paper][code]

24. [ICLR 2020] Strategies for Pre-training Graph Neural Networks.[paper][code]

25. [AAAI 2020] Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labels.[paper]

2019年论文列表

1. [KDD 2019 Workshop] SGR: Self-Supervised Spectral Graph Representation Learning.[paper]

2. [ICLR 2019 Workshop] Can Graph Neural Networks Go "Online"? An Analysis of Pretraining and Inference.[paper]

3. [ICLR 2019 workshop] Pre-Training Graph Neural Networks for Generic Structural Feature Extraction.[paper]

4. [Arxiv 2019] Heterogeneous Deep Graph Infomax[paper][code]

5. [ICLR 2019] Deep Graph Informax.[paper][code]

其他相关的工作

1. [Arxiv 2020] Self-supervised Learning: Generative or Contrastive.[paper]

2. [KDD 2020] Octet: Online Catalog Taxonomy Enrichment with Self-Supervision.[paper]

3. [WWW 2020] Structural Deep Clustering Network.[paper][code]

4. [IJCAI 2019] Pre-training of Graph Augmented Transformers for Medication Recommendation.[paper][code]

5. [AAAI 2020] Unsupervised Attributed Multiplex Network Embedding[paper][code]

6. [WWW 2020] Graph representation learning via graphical mutual information maximization[paper]

7. [NeurIPS 2017] Inductive Representation Learning on Large Graphs[paper][code]

8. [NeurIPS 2016 Workshop] Variational Graph Auto-Encoders[paper][code]

9. [WWW 2015] LINE: Large-scale Information Network Embedding[paper][code]

10. [KDD 2014] DeepWalk: Online Learning of Social Representations[paper][code]

更多内容请访问：

https://github.com/ChandlerBang/awesome-self-supervised-gnn

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