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 messages or multi-channel architectures, these efforts often fall short. A minority of studies attempt to train different node groups separately but suffer from inappropriate separation metrics 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 the 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 our framework can effectively separate nodes and yield significant performance improvement compared to the latest methods. The source code will be released soon.

翻译：图神经网络（GNNs）在图相关任务中取得了显著进展。然而，现实世界的图不可避免地包含一定比例的异嗜性节点，这挑战了经典GNN的同嗜性假设并阻碍了其性能。现有大多数研究仍试图设计异嗜性与同嗜性节点共享权重的通用模型。尽管引入了高阶消息或多通道架构，这些努力往往效果有限。少数研究尝试对不同节点组进行分离训练，但存在分离指标不当和效率低下的问题。本文首先提出一种新指标——邻域混乱度（NC），以实现更可靠的节点分离。我们观察到，具有不同NC值的节点组在组内准确率和可视化嵌入方面呈现出特定差异。这为基于邻域混乱度引导的图卷积网络（NCGCN）奠定了基础，该网络根据NC值对节点分组，并实现组内权重共享与消息传递。在同嗜性和异嗜性基准上的大量实验表明，我们的框架能有效分离节点，相较于最新方法取得显著的性能提升。源代码即将公开。