Graph Neural Network leveraging Higher-order Class Label Connectivity for Heterophilous Graphs

2026-06-05

AI Summary

Existing graph neural networks struggle to capture high-order class-label connectivity in heterophilic directed graphs, limiting their node classification performance. To address this, this work proposes the Label Context Classifier (LCC), which explicitly models high-order inter-class dependencies in heterophilic settings by generating label context embeddings through four types of directed walks. LCC incorporates an adaptive weighting mechanism that seamlessly integrates with any base GNN architecture. This approach is the first to explicitly encode high-order label structures under heterophily, achieving significant performance gains over state-of-the-art methods across multiple benchmark datasets and substantially improving node classification accuracy.

Abstract

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.

Problem

• heterophilous graphs
• node classification
• higher-order class label connectivity
• graph neural networks

Innovation

• heterophilous graphs
• higher-order connectivity
• label context embeddings
• graph neural networks
• node classification