Heterophily has been considered as an issue that hurts the performance of
Graph Neural Networks (GNNs). To address this issue, some existing work uses a
graph-level weighted fusion of the information of multi-hop neighbors to
include more nodes with homophily. However, the heterophily might differ among
nodes, which requires to consider the local topology. Motivated by it, we
propose to use the local similarity (LocalSim) to learn node-level weighted
fusion, which can also serve as a plug-and-play module. For better fusion, we
propose a novel and efficient Initial Residual Difference Connection (IRDC) to
extract more informative multi-hop information. Moreover, we provide
theoretical analysis on the effectiveness of LocalSim representing node
homophily on synthetic graphs. Extensive evaluations over real benchmark
datasets show that our proposed method, namely Local Similarity Graph Neural
Network (LSGNN), can offer comparable or superior state-of-the-art performance
on both homophilic and heterophilic graphs. Meanwhile, the plug-and-play model
can significantly boost the performance of existing GNNs. Our code is provided
at this https URL

通过引入 LocalSim 和 IRDC 提出了一种与节点 heterophily 差异相关联的本地相似性图神经网络 LSGNN，并在真实数据集上实现了超越同类方法的表现。