Zenglin Xu, Shirui Pan, Kijung Shin, Sunwoo Kim, Langzhang Liang, Yuan Qi
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Graph Neural Networks (GNNs) have gained significant attention as a powerful modeling and inference method, especially for homophilic graphstructured 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 messagepassing 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.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2405.20652")
get_code_for_paper("2405.20652")
have("2405.20652")
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