Leveraging Invariant Principle for Heterophilic Graph Structure Distribution Shifts

Jinluan Yang, Zhengyu Chen, Teng Xiao +3

Heterophilic Graph Neural Networks (HGNNs) have shown promising results for semi-supervised learning tasks on graphs. Notably, most real-world heterophilic graphs are composed of a…