Graph neural networks (GNNs) have achieved remarkable advances in
graph-oriented tasks. However, real-world graphs invariably contain a certain
proportion of heterophilous nodes, challenging the homophily assumption of
classical GNNs and hindering their performance. Most existing studies continue
to design generic models with shared weights between heterophilous and
homophilous nodes. Despite the incorporation of high-order message or
multi-channel architectures, these efforts often fall short. A minority of
studies attempt to train different node groups separately, but suffering from
inappropriate separation metric and low efficiency. In this paper, we first
propose a new metric, termed Neighborhood Confusion (NC), to facilitate a more
reliable separation of nodes. We observe that node groups with different levels
of NC values exhibit certain differences in intra-group accuracy and visualized
embeddings. These pave a way for Neighborhood Confusion-guided Graph
Convolutional Network (NCGCN), in which nodes are grouped by their NC values
and accept intra-group weight sharing and message passing. Extensive
experiments on both homophilous and heterophilous benchmarks demonstrate that
NCGCN can effectively separate nodes and offers significant performance
improvement compared to latest methods.

本文提出了一种名为邻域混淆（NC）的度量，用于可靠地分离异构节点，基于此指导图卷积网络（GNN）的设计，实现节点组的内部权重共享和信息传递，从而实现了在同质和异质基准测试上有效地分离和提高节点性能的目标。