通过结构相似性构建新图，提升异质图上GNN的性能
Adapting to Heterophilic Graph Data with Structure-Guided Neighbor Discovery
- 基于节点结构属性创建新连接图，增强标签同质性
- 在多个异质性数据集上达到顶尖或接近顶尖表现
- 适合处理标签与邻居不一致的复杂图数据
图神经网络（GNN）在异质图数据上表现不佳，因其通常依赖于同质性假设并依靠局部消息传递。为此，我们提出通过连接具有相似结构属性（如角色或全局特征）的节点来构建替代图结构，从而在新图上提升标签同质性。理论上证明，减少错误连接（不同类别间连接）的图能提升GNN性能，且多图视角增加找到有益结构的可能性。基于此，我们提出结构引导GNN（SG-GNN），同时处理原始图与新构建的结构图，自适应学习各图贡献权重。在多个基准数据集上的实验表明，尤其是具有异质特征的数据集，该方法达到当前最优或极具竞争力的表现，验证了利用结构信息引导GNN的有效性。
原文摘要 · Abstract (English)
Graph Neural Networks (GNNs) often struggle with heterophilic data, where connected nodes may have dissimilar labels, as they typically assume homophily and rely on local message passing. To address this, we propose creating alternative graph structures by linking nodes with similar structural attributes (e.g., role-based or global), thereby fostering higher label homophily on these new graphs. We theoretically prove that GNN performance can be improved by utilizing graphs with fewer false positive edges (connections between nodes of different classes) and that considering multiple graph views increases the likelihood of finding such beneficial structures. Building on these insights, we introduce Structure-Guided GNN (SG-GNN), an architecture that processes the original graph alongside the newly created structural graphs, adaptively learning to weigh their contributions. Extensive experiments on various benchmark datasets, particularly those with heterophilic characteristics, demonstrate that our SG-GNN achieves state-of-the-art or highly competitive performance, highlighting the efficacy of exploiting structural information to guide GNNs.
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