Papers about pretraining and self-supervised learning on Graph Neural Networks (GNN).
[WWW 2022] "SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation"
[ICML 2022] "ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learning"
[TKDE 2021] A PyTorch implementation of "Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection".
[IJCAI 2021] A PyTorch implementation of "Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning".
Papers about Graph Contrastive Learning and Graph Self-supervised Learning on Graphs with Heterophily
Code for ECML-PKDD 2022 paper "GraphMixup: Improving Class-Imbalanced Node Classification by Reinforcement Mixup and Self-supervised Context Prediction"
Source code of NeurIPS 2022 paper “Co-Modality Graph Contrastive Learning for Imbalanced Node Classification”
Code for ECML-PKDD 2023 paper "Learning to Augment Graph Structure for both Homophily and Heterophily Graphs"
Official code of our paper "Leveraging Joint Predictive Embedding and Bayesian Inference in Graph Self Supervised Learning"
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