Homophily on Human Interaction via. Stochastic Block Model network representing the likelihood of gathering of homophily people contributing to human connection with common likeliness by social status.
-
Updated
Nov 11, 2022 - Jupyter Notebook
Homophily on Human Interaction via. Stochastic Block Model network representing the likelihood of gathering of homophily people contributing to human connection with common likeliness by social status.
Neighbourhood standardization as a means to improve GNN performance heterophilic datasets.
Implementation of GCNH, a GNN for heterophilous graphs described in the paper "GCNH: A Simple Method For Representation Learning On Heterophilous Graphs", IJCNN 2023
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)
Papers about Graph Contrastive Learning and Graph Self-supervised Learning on Graphs with Heterophily
Gradient gating (ICLR 2023)
A Survey of Learning from Graphs with Heterophily
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.
Boost learning for GNNs from the graph structure under challenging heterophily settings. (NeurIPS'20)
This repository contains the resources on graph neural network (GNN) considering heterophily.
Add a description, image, and links to the heterophily topic page so that developers can more easily learn about it.
To associate your repository with the heterophily topic, visit your repo's landing page and select "manage topics."