Under circumstances of heterophily, where nodes with different labels tend to be connected based on semantic meanings, Graph Neural Networks (GNNs) often exhibit suboptimal performance. Current studies on graph heterophily mainly focus on aggregation calibration or neighbor extension and address the heterophily issue by utilizing node features or structural information to improve GNN representations. In this paper, we propose and demonstrate that the valuable semantic information inherent in heterophily can be utilized effectively in graph learning by investigating the distribution of neighbors for each individual node within the graph. The theoretical analysis is carried out to demonstrate the efficacy of the idea in enhancing graph learning. Based on this analysis, we propose HiGNN, an innovative approach that constructs an additional new graph structure, that integrates heterophilous information by leveraging node distribution to enhance connectivity between nodes that share similar semantic characteristics. We conduct empirical assessments on node classification tasks using both homophilous and heterophilous benchmark datasets and compare HiGNN to popular GNN baselines and SoTA methods, confirming the effectiveness in improving graph representations. In addition, by incorporating heterophilous information, we demonstrate a notable enhancement in existing GNN-based approaches, and the homophily degree across real-world datasets, thus affirming the efficacy of our approach.

在异质性的情况下，图神经网络（GNNs）往往表现出次优的性能。本文提出并证明了异质性中宝贵的语义信息可以通过研究每个节点在图中的邻居分布来有效地利用于图学习，进而构建了一种新的图结构HiGNN，该结构通过利用节点分布来增强具有相似语义特征的节点之间的连接性。通过在节点分类任务上的实证评估和与其他方法比较，我们验证了HiGNN在改进图表示方面的有效性，并证明通过整合异质性信息可以显著提升现有基于GNN的方法，以及在真实世界数据集中的同质性程度，从而证明了我们方法的功效。

异质友善信息增强图神经网络