graph-papers

Graph Neural Network, Self-Supervised Learning, Contrastive Learning, RecSys, Transformer Papers Reading Notes.

Updating~

1. Survey or Benchmark

• [ ] arXiv'22 A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions [Paper] [Code] [Link]
• [ ] arXiv'21 Cross-Domain Recommendation: Challenges, Progress, and Prospects [Paper] [Code] [Link]
• [ ] TKDE'22 Self-Supervised Learning for Recommender Systems: A Survey [Paper] [Code] [Link]
• [x] TKDE'22 Graph Self-Supervised Learning: A Survey [Paper] [Code] [Link]
• [x] TKDE'21 Self-supervised Learning on Graphs: Contrastive, Generative,or Predictive [Paper] [Code] [Link]
• [x] TKDE'21 Self-supervised Learning: Generative or Contrastive [Paper] [Code] [Link]
• [x] Dataset@NIPS'21 An Empirical Study of Graph Contrastive Learning [Paper] [Code] [Link]
• [ ] arXiv'21 A Survey of Transformers [Paper] [Code] [Link]

[Paper] [Code] [Link]

2. Graph Embedding

• [ ] KDD'17 metapath2vec: Scalable Representation Learning for Heterogeneous Networks [Paper] [Code] [Link]
• [ ] KDD'16 SDNE: Structural Deep Network Embedding [Paper] [Code] [Link]
• [ ] KDD'16 node2vec: Scalable Feature Learning for Networks [Paper] [Code] [Link]
• [ ] WWW'15 LINE: Large-scale Information Network Embedding [Paper] [Code] [Link]
• [x] KDD'14 DeepWalk: Online Learning of Social Representations [Paper] [Code] [Link]
• [x] NeurIPS'13 word2vec: Distributed Representations of Words and Phrases and their Compositionality [Paper] [Code] [Link]

[Paper] [Code] [Link]

3. Data Augment

• [ ] WWW'22 Augmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practices [Paper] [Code] [Link]
• [ ] arXiv'22 Data Augmentation for Deep Graph Learning: A Survey [Paper] [No Code] [Link]
• [ ] WWW'21 Mixup for Node and Graph Classification [Paper] [Code] [Link]
• [x] WSDM'21 GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks [Paper] [Code] [Link]

[Paper] [Code] [Link]

4. Self-supervised Learning -- Contrastive

Methods

• [ ] ICML'23 Disentangled Multiplex Graph Representation Learning [Paper] [Code] [Link]
• [ ] AAAA'23 Multiplex Graph Representation Learning via Common and Private Information Mining [Paper] [Code] [Link]
• [ ] ICLR'23 A Message Passing Perspective on Learning Dynamics of Contrastive Learning [Paper] [Code] [Link]
• [x] ICLR'23 Link Prediction with Non-Contrastive Learning [Paper] [Code] [Link]
• [ ] AAAI'23 Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily Discriminating [Paper] [Code] [Link]
• [ ] AAAI'23 MA-GCL: Model Augmentation Tricks for Graph Contrastive Learning [Paper] [Code] [Link]
• [ ] AAAI'23 SFA: Spectral Feature Augmentation for Graph Contrastive Learning and Beyond [Paper] [Code] [Link]
• [x] AAAI'22 AFGRL: Augmentation-Free Self-Supervised Learning on Graphs [Paper] [Code] [Link]
• [x] NIPS'22 GGD: Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination [Paper] [Code] [Link]
• [x] AAAA'22 SUGRL: Simple Unsupervised Graph Representation Learning [Paper] [Code] [Link1] [Link2]
• [ ] TNNLS'22 Prototypical Graph Contrastive Learning [Paper] [Code] [Link]
• [ ] ICML'21 JOAO: Graph Contrastive Learning Automated [Paper] [Code] [Link]
• [ ] IJCAI'21 Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning [Paper] [Code] [Link]
• [x] Workshop@ICLR'21 BRGL: Bootstrapped Representation Learning on Graphs [Paper] [Code] [Link]
• [x] NIPS'21 CCA-SSG: From Canonical Correlation Analysis to Self-supervised Graph Neural Networks [Paper] [Code] [Link]
• [x] WWW'21 GCA: Graph Contrastive Learning with Adaptive Augmentation [Paper] [Code] [Link]
• [x] ICML'20 MVGRL: Contrastive Multi-View Representation Learning on Graphs [Paper] [Code] [Link]
• [x] Workshop@ICML'20 GRACE: Deep Graph Contrastive Representation Learning [Paper] [Code] [Link]
• [x] ICLR'20 InfoGraph: Unsupervised and Semi-Supervised Graph-level Representation Learning via Mutual Information Maximization [Paper] [Code] [Link]
• [x] NIPS'20 GraphCL: Graph Contrastive Learning with Augmentations [Paper] [Code] [Link]
• [x] NIPS'20 Supervised Contrastive Learning [Paper] [Code] [Link1] [Link2]
• [x] WWW'20 GMI: Graph Representation Learning via Graphical Mutual Information Maximization [Paper] [Code] [Link]
• [x] ICLR'19 DGI: Deep Graph Infomax [Paper] [Code] [Link]

Theories:

• [ ] ICLR'23 A theoretical study of inductive biases in contrastive learning [Paper] [Code] [Link] [OpenReview]
• [ ] ICLR'23 Towards the Generalization of Contrastive Self-Supervised Learning [Paper] [Code] [Link] [OpenReview]
• [ ] CVPR'22 Node Representation Learning in Graph via Node-to-Neighbourhood Mutual Information Maximization [Paper] [Code] [Link]
• [x] ECCV'22 Decoupled Contrastive Learning [Paper] [Code] [Link]
• [ ] ICLR'22 Equivariant Contrastive Learning [Paper] [Code] [Link]
• [ ] ICLR'21 What Should Not Be Contrastive in Contrastive Learning [Paper] [Code] [Link]
• [ ] ICML'21 Understanding Self-Supervised Learning Dynamics without Contrastive Pairs [Paper] [Code] [Link]
• [ ] CVPR'21 Understanding the Behaviour of Contrastive Loss [Paper] [Code] [Link]
• [x] ICML'20 Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere [Paper] [Code] [Link1] [Link2]

[Paper] [Code] [Link]

5. Self-Supervised Learning -- Generative

• [x] KDD'22 GraphMAE: Self-Supervised Masked Graph Autoencoders [Paper] [Code] [Link]
• [x] arXiv'22 MaskGAE: Masked Graph Modeling Meets Graph Autoencoders [Paper] [Code] [Link]

[Paper] [Code] [Link]

6. Self-supervised Learning -- Pretrain

• [x] ICLR'20 Strategies for Pre-training Graph Neural Networks [Paper] [Code] [Link]
• [x] KDD'20 GCC: Graph Contrastive Coding for Graph Neural Network Pre-training [Paper] [Code] [Link]

[Paper] [Code] [Link]

7. Heterogeneous Graph

• [ ] WWW'20 Heterogeneous Graph Transformer [Paper] [Code] [Link]

[Paper] [Code] [Link]

8. Heterophonic Graph （Heterophily）

• [ ] NIPS'22 Revisiting Heterophily For Graph Neural Networks [Paper] [Code] [Link]
• [x] IJCAI'22 Beyond Homophily: Structure-aware Path Aggregation Graph Neural Network [Paper] [Code] [Link]

[Paper] [Code] [Link]

9. Graph Anomaly Detection

• [ ] ICML'22 Rethinking Graph Neural Networks for Anomaly Detection [Paper] [Code] [Link]

[Paper] [Code] [Link]

10. NLP And CV And RecSys In Contrastive Learning

Computer Vision：

• [ ] InsDis: Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination [Paper] [Code] [Link]
• [x] Deep Infomax: Learning deep representations by mutual information estimation and maximization [Paper] [Code] [Link]
• [ ] CPC: Representation Learning with Contrastive Predictive Coding [Paper] [Code] [Link]
• [x] CMC: Contrastive Multiview Coding [Paper] [Code] [Link]
• [ ] InfoMin: What Makes for Good Views for Contrastive Learning? [Paper] [Code] [Link1] [Link2]
• [ ] PCL: Prototypical Contrastive Learning of Unsupervised Representations [Paper] [Code] [Link]
• [x] MoCo v1: Momentum Contrast for Unsupervised Visual Representation Learning [Paper] [Code] [Link]
• [x] SimCLR v1: A Simple Framework for Contrastive Learning of Visual Representations [Paper] [Code] [Link]
• [x] MoCo v2: Improved Baselines with Momentum Contrastive Learning [Paper] [Code] [Link]
• [x] SimCLR v2: Big Self-Supervised Models are Strong Semi-Supervised Learners [Paper] [Code] [Link]
• [x] SwAV: Unsupervised Learning of Visual Features by Contrasting Cluster Assignments [Paper] [Code] [Link]
• [x] BYOL: Bootstrap your own latent: A new approach to self-supervised Learning [Paper] [Code] [Link]
• [x] SimSiam: Exploring Simple Siamese Representation Learning [Paper] [Code] [Link]
• [ ] MoCo v3: An Empirical Study of Training Self-Supervised Vision Transformers [Paper] [Code] [Link]
• [x] Barlow Twins: Self-Supervised Learning via Redundancy Reduction [Paper] [Code] [Link]
• [ ] VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning [Paper] [Code] [Link]
• [x] MEC: Self-Supervised Learning via Maximum Entropy Coding [Paper] [Code] [Link]
• [ ] ICCV'21 Weakly Supervised Contrastive Learning [Paper] [Code] [Link]
• [ ] PMLR'21 Dissecting Supervised Contrastive Learning [Paper] [Code] [Link]
• [ ] CVPR'22 Targeted Supervised Contrastive Learning for Long-Tailed Recognition [Paper] [Code] [Link]
• [ ] ArXiv'23 EMP-SSL: Towards Self-Supervised Learning in One Training Epoch [Paper] [Code] [Link]

[Paper] [Code] [Link]

Natural Language Processing：

• [x] SimCSE: Simple Contrastive Learning of Sentence Embeddings [Paper] [Code] [Link]

[Paper] [Code] [Link]

Recommender System：

• [ ] WSDM'23 Heterogeneous Graph Contrastive Learning for Recommendation [Paper] [Code] [Link]
• [x] SIGIR'22 SimGCL: Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation [Paper] [Code] [Link]
• [x] WWW'22 NCL: Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning [Paper] [Code] [Link]
• [ ] KDD'22 DirectAU: Towards Representation Alignment and Uniformity in Collaborative Filtering [Paper] [Code] [Link]
• [x] SIGIR'21 SGL: Self-supervised Graph Learning for Recommendation [Paper] [Code] [Link]
• [ ] SIGIR'21 Bootstrapping User and Item Representations for One-Class Collaborative Filtering [Paper] [Code] [Link]
• [ ] KDD'21 MixGCF: An Improved Training Method for Graph Neural Network-based Recommender Systems [Paper] [Code] [Link]

[Paper] [Code] [Link]

11. RecSys

Cross Domain

• [ ] WWW'23 Cross-domain recommendation via user interest alignment [Paper] [Code] [Link]

• [ ] WSDM'23 One for All, All for One: Learning and Transferring User Embeddings for Cross-Domain Recommendation [Paper] [Code] [Link]

• [ ] WSDM'22 RecGURU: Adversarial Learning of Generalized User Representations for Cross-Domain Recommendation [Paper] [Code] [Link]

• [ ] SDM'22 Localized Graph Collaborative Filtering [Paper] [Code] [Link]

• [x] CIKM'21 UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation [Paper] [Code] [Link1] [Link2]

• [x] SIGIR'20 LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation [Paper] [Code] [Link]

• [ ] KDD'20 Embedding-based Retrieval in Facebook Search [Paper] [Code] [Link]

• [x] SIGIR‘19 NGCF: Neural Graph Collaborative Filtering [Paper] [Code] [Link]

• [ ] KDD'18 PinSAGE: Graph Convolutional Neural Networks for Web-Scale Recommender Systems [Paper] [Code] [Link]

[Paper] [Code] [Link]

12. Graph Model

References: https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#convolutional-layers

• [ ] GCN
• [ ] Chebnet
• [ ] GraphSAGE
• [ ] GraphConv
• [ ] APPNP
• [ ] GAT
• [ ] Transformer

[Paper] [Code] [Link]

13. Transformer

nlp:

cv:

• [ ] TPAMI'22 Pyramid Pooling Transformer for Scene Understanding [Paper] [Code] [Link]
• [ ] AAAI'22 Less is More: Pay Less Attention in Vision Transformers [Paper] [Code] [Link]
• [ ] ICML'21 ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision [Paper] [Code] [Link]
• [ ] ICLR'21 ViT: An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale [Paper] [Code] [Link]
• [ ] ICCV'21 Swin Transformer: Hierarchical Vision Transformer using Shifted Windows [Paper] [Code] [Link]
• [ ] ICCV'20 DETR: End-to-End Object Detection with Transformers [Paper] [Code] [Link]

[Paper] [Code] [Link]

graph:

Papers about graph transformers: awesome-graph-transformer

• [ ] ICLR'23 NAGphormer: A Tokenized Graph Transformer for Node Classification in Large Graphs [Paper] [Code] [Link]
• [ ] NIPS'22 Hierarchical Graph Transformer with Adaptive Node Sampling [Paper] [Code] [Link]
• [ ] NIPS'22 Recipe for a General, Powerful, Scalable Graph Transformer [Paper] [Code] [Link]
• [x] NIPS'22 NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification [Paper] [Code] [Link]
• [ ] WWW'22 Universal Graph Transformer Self-Attention Networks [Paper] [Code] [Link]
• [x] Workshop@AAAI'21 A Generalization of Transformer Networks to Graphs [Paper] [Code] [Link]

[Paper] [Code] [Link]