Discovering Invariant Neighborhood Patterns for Heterophilic Graphs
Ruihao Zhang Zhengyu Chen Teng Xiao Yueyang Wang Kun Kuang
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 same class are more likely to be linked.
However, such assumptions of homophily do not always hold in real-world graphs,
which leads to more complex distribution shifts unaccounted for in previous
methods. The distribution shifts of neighborhood patterns are much more diverse
on non-homophilous graphs. We propose a novel Invariant Neighborhood Pattern
Learning (INPL) to alleviate the distribution shifts problem on non-homophilous
graphs. Specifically, we propose the Adaptive Neighborhood Propagation (ANP)
module to capture the adaptive neighborhood information, which could alleviate
the neighborhood pattern distribution shifts problem on non-homophilous graphs.
We propose Invariant Non-Homophilous Graph Learning (INHGL) module to constrain
the ANP and learn invariant graph representation on non-homophilous graphs.
Extensive experimental results on real-world non-homophilous graphs show that
INPL could achieve state-of-the-art performance for learning on large
non-homophilous graphs.