GLANCE: Graph Logic Attention Network with Cluster Enhancement for Heterophilous Graph Representation Learning
This addresses the challenge of learning from heterophilous graphs for applications in graph-structured data analysis, though it appears incremental as it builds on existing GNN methods with specific enhancements.
The paper tackles the problem of Graph Neural Networks struggling with heterophilous graphs by proposing GLANCE, a framework integrating logic-guided reasoning, dynamic graph refinement, and adaptive clustering, achieving competitive performance on benchmark datasets like Cornell, Texas, and Wisconsin.
Graph Neural Networks (GNNs) have demonstrated significant success in learning from graph-structured data but often struggle on heterophilous graphs, where connected nodes differ in features or class labels. This limitation arises from indiscriminate neighbor aggregation and insufficient incorporation of higher-order structural patterns. To address these challenges, we propose GLANCE (Graph Logic Attention Network with Cluster Enhancement), a novel framework that integrates logic-guided reasoning, dynamic graph refinement, and adaptive clustering to enhance graph representation learning. GLANCE combines a logic layer for interpretable and structured embeddings, multi-head attention-based edge pruning for denoising graph structures, and clustering mechanisms for capturing global patterns. Experimental results in benchmark datasets, including Cornell, Texas, and Wisconsin, demonstrate that GLANCE achieves competitive performance, offering robust and interpretable solutions for heterophilous graph scenarios. The proposed framework is lightweight, adaptable, and uniquely suited to the challenges of heterophilous graphs.