  While Graph Neural Networks (GNNs) have achieved enormous success in multiple
graph analytical tasks, modern variants mostly rely on the strong inductive
bias of homophily. However, real-world networks typically exhibit both
homophilic and heterophilic linking patterns, wherein adjacent nodes may share
dissimilar attributes and distinct labels. Therefore, GNNs smoothing node
proximity holistically may aggregate both task-relevant and irrelevant (even
harmful) information, limiting their ability to generalize to heterophilic
graphs and potentially causing non-robustness. In this work, we propose a novel
Edge Splitting GNN (ES-GNN) framework to adaptively distinguish between graph
edges either relevant or irrelevant to learning tasks. This essentially
transfers the original graph into two subgraphs with the same node set but
complementary edge sets dynamically. Given that, information propagation
separately on these subgraphs and edge splitting are alternatively conducted,
thus disentangling the task-relevant and irrelevant features. Theoretically, we
show that our ES-GNN can be regarded as a solution to a disentangled graph
denoising problem, which further illustrates our motivations and interprets the
improved generalization beyond homophily. Extensive experiments over 11
benchmark and 1 synthetic datasets not only demonstrate the effective
performance of ES-GNN but also highlight its robustness to adversarial graphs
and mitigation of the over-smoothing problem.
