Graph Neural Networks for Graphs with Heterophily: A Survey

Understand

Recent years have witnessed fast developments of graph neural networks (GNNs) that have benefited myriad graph analytic tasks and applications.

• Most GNNs rely on the homophily assumption that nodes belonging to the same class are more likely to be connected.
• However, as a ubiquitous graph property in numerous real-world scenarios, heterophily, i.e., nodes with different labels tend to be linked, significantly limits the performance of tailor-made homophilic GNNs.
• Hence, GNNs for heterophilic graphs are gaining increasing research attention to enhance graph learning with heterophily.