  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. We first identify two limitations of SMP: undesirable
representation update for multi-hop neighbors and vulnerability against
oversmoothing issues. To overcome these challenges, we propose a novel message
passing function called Multiset to Multiset GNN(M2M-GNN). Our theoretical
analyses and extensive experiments demonstrate that M2M-GNN effectively
alleviates the aforementioned limitations of SMP, yielding superior performance
in comparison
