Sign is Not a Remedy: Multiset-to-Multiset Message Passing for Learning on Heterophilic Graphs

Understand

Graph Neural Networks (GNNs) have gained significant attention as a powerful modeling and inference method, especially for homophilic graph-structured data.

• To empower GNNs in heterophilic graphs, where adjacent nodes exhibit dissimilar labels or features, Signed Message Passing (SMP) has been widely adopted.
• However, there is a lack of theoretical and empirical analysis regarding the limitations of SMP.
• In this work, we unveil some potential pitfalls of SMP and their remedies.