异配图分类是GNN的长期痛点，LCC用高阶标签连通性解决了这个问题，做图分析或社交网络研究的开发者可以直接参考实验方法。
图神经网络在同配图上表现优异，但在异配图（不同类节点更易相连）上性能受限。现有GNN无法捕捉异配图中常见的高阶类标签连通性。研究者提出标签上下文分类器（LCC），通过四种随机游走生成标签上下文嵌入，捕获高阶标签连通性。LCC可与任意GNN集成，并自适应学习其重要性。实验表明，集成LCC的GNN在异配有向图上超越现有方法。
Graph Neural Network leveraging Higher-order Class Label Connectivity for Heterophilous Graphs
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.