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Multi-graph contrastive learning for cancer driver gene identification
The code repository of "Towards Deep Attention in Graph Neural Networks: Problems and Remedies," published in ICML 2023.
AI for Science 论文解读合集（持续更新ing），论文/数据集/教程下载：hyper.ai
Official PyTorch implementation of "Towards Deeper Graph Neural Networks" [KDD2020]
Reimplementation of AAAI21 paper "Beyond Low-frequency Information in Graph Convolutional Networks" based on PyTorch and PyTorch Geometric (PyG).
GraphACL: Simple and Asymmetric Graph Contrastive Learning (NeurIPS 2023)
This repository contains the resources on graph neural network (GNN) considering heterophily.
GraphCDR: A graph neural network method with contrastive learning for cancer drug response prediction
Must-read papers on graph neural networks (GNN)
Implementation for the paper MoCL: Contrastive Learning on Molecular Graph with multi-level Domain Knowledge
Chemical-Reaction-Aware Molecule Representation Learning
Hierarchical Inter-Message Passing for Learning on Molecular Graphs
Implementation of MolCLR: "Molecular Contrastive Learning of Representations via Graph Neural Networks" in PyG.
Python package built to ease deep learning on graph, on top of existing DL frameworks.
Drug repositioning with adaptive graph convolutional networks
A curated list of papers on graph structure learning (GSL).
A curated list of Heterophilous Graph Self-Supervised Learning papers.
[AAAI'23] Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily Discriminating
Geometric Deep Learning Extension Library for PyTorch