/Awesome-Graph-Contrastive-Learning

Collection of resources related with Graph Contrastive Learning.

GNU General Public License v3.0GPL-3.0

Awesome-Graph-Contrastive-Learning

Collection of resources related with Graph Contrastive Learning.

Contents

Papers

2019

  1. Deep Graph Infomax Petar Veličković, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, & R Devon Hjelm. ICLR 2019.
    • Paper: OpenReview | Code: PyTorch, tf_geometric
    • Method(DGI): Local-Global Mutual Information Maximization
    • Experiment:
      • Task: Transductive Node Classification | Datasets: Cora, Citeseer, Pubmed. | Baselines: Raw features, Label Propagation, DeepWalk, DeepWalk + features, GCN, Planetoid.
      • Task: Inductive Node Classification | Datasets: Reddit, PPI. | Baselines: Raw features; DeepWalk; DeepWalk + features; GraphSAGE-GCN; GraphSAGE-mean; GraphSAGE-LSTM; GraphSAGE-pool; FastGCN; Avg. pooling.

2020

  1. InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization Fan-Yun Sun, Jordan Hoffmann, Vikas Verma, Jian Tang. ICLR 2020.

    • Paper: OpenReview | Code: PyTorch
    • Method: Local-Global Mutual Information Maximization (Batch-wise Negative Sampling, Multi-scale), GIN
    • Experiment:
      • Task: Graph Classification | Datasets: MUTAG, PTC-MR, RDT-B, RDT-M5K, IMDB-B, IMDB-M. | Baselines: RandomWalk, Shortest Path Kernel, Graphlet Kernel, Weisfeiler-Lehman Sub-tree Kernel, Deep Graph Kernels, Multi-Scale Laplacian Kernel, node2vec, sub2vec, graph2vec.
      • Task: Semi-supervised Molecular Property Prediction | Datasets: QM9. | Baselines: Mean-Teachers.

  2. Contrastive Multi-View Representation Learning on Graphs Kaveh Hassani, Amir Hosein Khas Ahmadi. ICML 2020.

    • Paper: MLR | Code: PyTorch
    • Method(MVGRL): Multi-View Local-Global Mutual Information Maximization
    • Experiment:
      • Task: Node Classification | Datasets: Cora, Citeseer, Pubmed. | Baselines: MLP, Iterative Classification Algorithm, Label Propagation, ManiReg, SemiEmb, Planetoid, Chebyshev, GCN, MoNet, JKNet, GAT, Linear, DeepWalk, GAE, VERSE, DGI.
      • Task: Node Clustering | Datasets: Cora, Citeseer, Pubmed. | Baselines: VGAE, MGAE, ARGA, ARVGA, GALA.
      • Task: Graph Classification | Datasets: MUTAG, PTC-MR, IMDB-BIN, IMDB-MULTI, REDDIT-BIN. | Baselines: Shortest Path Kernel, Graphlet Kernel, Weisfeiler-Lehman Sub-tree Kernel, Deep Graph Kernels, Multi-scale Laplacian Kernel, GraphSAGE, GIN-0, GIN-ε, GAT, RandomWalk, node2vec, sub2vec, graph2vec, InfoGraph.

  3. GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training Jiezhong Qiu, Qibin Chen, Yuxiao Dong, Jing Zhang, Hongxia Yang, M. Ding, Kuansan Wang, Jie Tang. KDD 2020.

  4. Graph Contrastive Learning with Augmentations Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, Yang Shen. NeurIPS 2020.

  5. Unsupervised Graph Representation by Periphery and Hierarchical Information Maximization Sambaran Bandyopadhyay, Manasvi Aggarwal, M. Murty. Arxiv 2020.

    • Paper: Arxiv
    • Method(GraPHmax): Periphery Information Maximization, Hierarchical Information Maximization
    • Experiment:
      • Task: Graph Classification | Datasets: MUTAG, PTC, PROTEINS, NCI1 and NCI09, IMDB-BINARY, IMDB-MULTI. | Baselines: Graphlet Kernel, RandomWalk Graph Kernel, Propagation Kernels, Weisfeiler-lehman Graph Kernels, AWE-DD, AWE-FB, DGCNN, PSCN, DCNN, ECC, DGK, DiffPool, IGN, GIN, 1-2-3GNN, 3WL-GNN, node2vec, sub2vec, graph2vec, DGI, InfoGraph.
      • Task: Graph Clustering | Datasets: MUTAG, PROTEINS, IMDB-M. | Baselines: DGI, InfoGraph.

  6. Graph Representation Learning via Graphical Mutual Information Maximization Zhen Peng, Wenbing Huang, Minnan Luo, Qinghua Zheng, Yu Rong, Tingyang Xu, Junzhou Huang. WWW 2020.

    • Paper: ACM, Arxiv | Code: PyTorch
    • Method(GMI): Graphical Mutual Information
    • Experiment:
      • Task: Transductive Node Classification | Datasets: Cora, Citeseer, Pubmed. | Baselines: Raw Features, DeepWalk, EP-B, DGI, LP, Planetoid-T, GCN, GAT, GWNN.
      • Task: Inductive Node Classification | Datasets: Reddit, PPI. | Baselines: Raw Features, DeepWalk, DeepWalk+Features, GraphSAGE-GCN, GraphSAGE-Mean, GraphSAGE-LSTM, GraphSAGE-Pool, DGI, GAT, FastGCN, GaAN.
      • Task: Link Prediction | Datasets: Cora, BlogCatalog, Flickr, PPI. | Baselines: DGI.

2021

  1. Bipartite Graph Embedding via Mutual Information Maximization Jiangxia Cao, Xixun Lin, Shu Guo, Luchen Liu, Tingwen Liu, Bin Wang. WSDM 2021.

    • Paper: Arxiv
    • Method(BiGI): Local-Global Mutual Information Maximization, H-hop Enclosing Subgraph
    • Experiment:
      • Task: Top-K Recommendation | Datasets: DBLP, ML-100K and ML-10M. | Baselines: DeepWalk, LINE, Node2vec, VGAE, Metapath2vec, DMGI, PinSage, BiNE, GC-MC, IGMC, NeuMF, NGCF.
      • Task: Link Prediction | Datasets: Wikipedia. | Baselines: DeepWalk, LINE, Node2vec, VGAE, Metapath2vec, DMGI, PinSage, BiNE, GC-MC, IGMC, NeuMF, NGCF.

  2. How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision Dongkwan Kim, Alice Oh. ICLR 2021

    • Paper: OpenReview | Code: torch_geometric
    • Method(SuperGAT):
    • Experiment:
      • Task: Node Classification | Datasets: ogbn-arxiv, CS, Physics, Cora-ML, Cora-Full, DBLP, Chameleon, Four-Univ, Wiki-CS, Photo, Computers, Flickr, Crocodile, Cora, CiteSeer, PubMed, PPI. | Baselines: GCN, GraphSAGE, GAT, CGAT, GLCN, LDS, GCN + GAM, GCN + NS.
      • Task: Link Prediction | Datasets: Cora, CiteSeer, PubMed, PPI. | Baselines:

Library