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"

[Preprint]Official Code of our paper Predict, Cluster, Refine: A Joint Embedding Predictive Self-Supervised Framework for Graph Representation Learning

Disentangle music audio into independent melody, rhythm, and timbre signals to compute precise multi-factor similarity scores.