NeurIPS 2022, Revisiting Heterophily For Graph Neural Networks, official PyTorch implementation for Adaptive Channel Mixing (ACM) GNN framework
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Updated
Sep 27, 2023 - Python
NeurIPS 2022, Revisiting Heterophily For Graph Neural Networks, official PyTorch implementation for Adaptive Channel Mixing (ACM) GNN framework
Dir-GNN is a machine learning model that enables learning on directed graphs.
Gradient gating (ICLR 2023)
Implementation of GCNH, a GNN for heterophilous graphs described in the paper "GCNH: A Simple Method For Representation Learning On Heterophilous Graphs", IJCNN 2023
Neighbourhood standardization as a means to improve GNN performance heterophilic datasets.
How does Heterophily Impact the Robustness of Graph Neural Networks? Theoretical Connections and Practical Implications (KDD'22)
Code for GBK-GNN (paper accepted by WWW2022)
Boost learning for GNNs from the graph structure under challenging heterophily settings. (NeurIPS'20)
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