用结构特征重连图，让GNN更好处理标签不相似的图数据。
Structure-Guided Input Graph for GNNs facing Heterophily
- 基于节点结构特征（如度、中心性）构建新邻接关系
- 在异质图上标签更平滑，分类准确率显著提升
- 适合标签分布不均匀的复杂网络分析场景
图神经网络（GNN）在处理非规则结构数据方面表现出色，但多数架构仅在同质图（邻居标签相似）上表现良好。针对异质图（邻居标签差异大）的问题，本文提出基于结构特征构建新图：通过计算节点间结构特征距离，建立k近邻图，其中结构相似的节点被连接。结构特征包括角色类（如度）和全局类（如中心性）。实验表明，新图中标签更平滑，使用该结构的GNN性能显著提升。
原文摘要 · Abstract (English)
Graph Neural Networks (GNNs) have emerged as a promising tool to handle data exhibiting an irregular structure. However, most GNN architectures perform well on homophilic datasets, where the labels of neighboring nodes are likely to be the same. In recent years, an increasing body of work has been devoted to the development of GNN architectures for heterophilic datasets, where labels do not exhibit this low-pass behavior. In this work, we create a new graph in which nodes are connected if they share structural characteristics, meaning a higher chance of sharing their labels, and then use this new graph in the GNN architecture. To do this, we compute the k-nearest neighbors graph according to distances between structural features, which are either (i) role-based, such as degree, or (ii) global, such as centrality measures. Experiments show that the labels are smoother in this newly defined graph and that the performance of GNN architectures improves when using this alternative structure.
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