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

图神经网络广受关注，特别是对于同构图数据有强大的建模和推理能力。为了增强在异构图中的性能，我们发现了一些带有潜在风险的 Signed Message Passing 的局限，并提出了一种新的消息传递方法 M2M-GNN，通过理论分析和实验证明 M2M-GNN 在解决 SMP 的局限性方面效果显著。

符号非疗法：用于异构图学习的多集合到多集合信息传递