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This repository contains the resources on graph neural network (GNN) considering heterophily.
Created
2021-12-06
115 commits to main branch, last one about a month ago
Boost learning for GNNs from the graph structure under challenging heterophily settings. (NeurIPS'20)
Created
2020-10-16
9 commits to master branch, last one 2 years ago
A Survey of Learning from Graphs with Heterophily
Created
2023-12-19
78 commits to main branch, last one about a month ago
Dir-GNN is a machine learning model that enables learning on directed graphs.
Created
2023-05-25
5 commits to main branch, last one about a year ago
NeurIPS 2022, Revisiting Heterophily For Graph Neural Networks, official PyTorch implementation for Adaptive Channel Mixing (ACM) GNN framework
Created
2022-11-21
6 commits to main branch, last one about a year ago
Gradient gating (ICLR 2023)
Created
2022-10-03
16 commits to main branch, last one about a year ago
Papers about Graph Contrastive Learning and Graph Self-supervised Learning on Graphs with Heterophily
Created
2023-11-02
19 commits to main branch, last one about a year ago