提升异质图节点分类效果，捕捉标签间的高阶连接关系。
Graph Neural Network leveraging Higher-order Class Label Connectivity for Heterophilous Graphs
- 设计标签上下文分类器，通过四种路径生成标签上下文嵌入。
- 在异质有向图上，集成LCC的GNN显著优于现有方法。
- 适合处理标签不相似节点更易相连的复杂图数据。
图神经网络（GNN）在同质图中表现优异，但面对异质图时性能受限，因异质图中不同标签节点更可能相连。现有基于图卷积的GNN无法捕捉这种高阶标签连接模式。为此，本文提出标签上下文分类器（LCC），通过四种不同类型的路径生成标签上下文嵌入，以估计目标节点的标签。同时，该方法可自适应地融合任意GNN与LCC，动态学习二者重要性。实验表明，集成LCC的GNN在异质有向图上显著超越当前最优方法，标签上下文嵌入有效提升了分类性能。
原文摘要 · Abstract (English)
Node classification in graph neural networks (GNNs) has been widely applied in various fields of graph analysis. GNNs achieve high-accuracy node classification in homophilous graphs, where nodes with the same class label tend to be connected. However, their performance remains limited in heterophilous graphs, where nodes with different class labels are more likely to be connected. In particular, current GNNs derived from graph convolutional networks cannot capture higher-order class label connectivity, which is frequently observed in real-world heterophilous graphs. To address this issue, we propose a novel classifier, Label Context Classifier (LCC), designed to capture higher-order class label connectivity in directed graphs. LCC estimates the class label of a target node by leveraging label context embeddings that are generated through four distinct types of walks. In addition, our approach allows the integration of LCC and any GNN by adaptively learning their importance. Experimental results demonstrate that GNNs integrated with LCC outperform SOTA methods and the label context embeddings improve the node classification performance in heterophilous directed graphs.
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