Heterophilic Graph

Heterophilic graphs, where connected nodes tend to have dissimilar features or labels, pose a significant challenge to traditional graph neural networks (GNNs) designed for homophilic data. Current research focuses on developing GNN architectures and algorithms that effectively handle this heterophily, including methods that adapt message passing, employ personalized node-specific aggregation, and leverage auxiliary information like node attributes or textual data. Overcoming this limitation is crucial for advancing GNN applications in diverse fields, as many real-world networks exhibit significant heterophily, hindering the performance of standard GNN approaches.