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 traditional 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 available in https://github.com/GISec-Team/NCGNN.

翻译：图神经网络（GNNs）在图相关任务中取得了显著进展。然而，现实世界的图总是包含一定比例的异配节点，这对传统GNNs的同配性假设提出了挑战，并阻碍了其性能。大多数现有研究仍在设计异配节点与同配节点之间共享权重的通用模型。尽管引入了高阶信息或多通道架构，这些努力往往收效甚微。少数研究尝试对不同节点组进行分别训练，但受限于不恰当的分离度量标准与较低效率。本文首先提出一种称为邻域混淆（NC）的新度量标准，以实现更可靠的节点分离。我们观察到，具有不同NC值水平的节点组在组内准确率和可视化嵌入方面表现出一定差异。这为邻域混淆引导的图卷积网络（NCGCN）奠定了基础，在该网络中，节点按其NC值进行分组，并接受组内权重共享与信息传递。在同配性与异配性基准数据集上的大量实验表明，我们的框架能够有效分离节点，并与最新方法相比带来显著的性能提升。源代码将在 https://github.com/GISec-Team/NCGNN 提供。