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"
[TKDE 2021] A PyTorch implementation of "Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection".
[ICML 2022] "ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learning"
[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"
Add a description, image, and links to the graph-self-supervised-learning topic page so that developers can more easily learn about it.
To associate your repository with the graph-self-supervised-learning topic, visit your repo's landing page and select "manage topics."