2 papers
cs.LG2025
Discovering Invariant Neighborhood Patterns for Heterophilic Graphs
Jinluan Yang, Ruihao Zhang, Zhengyu Chen +4
This paper studies the problem of distribution shifts on non-homophilous graphs Mosting existing graph neural network methods rely on the homophilous assumption that nodes from the…
cs.LG2025
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…