GLANCE: Graph Logic Attention Network with Cluster Enhancement for Heterophilous Graph Representation Learning

2025-07-24

AI Summary

Traditional graph neural networks (GNNs) suffer significant performance degradation on heterophilous graphs, where node features and labels exhibit low correlation within local neighborhoods. To address this, we propose a novel representation learning framework integrating logical reasoning, dynamic graph optimization, and adaptive clustering. Methodologically: (1) a logic-guided embedding layer explicitly encodes structured prior knowledge; (2) a multi-head attention–driven edge pruning mechanism dynamically denoises the graph structure; and (3) a clustering-enhanced module captures global topological patterns while suppressing structural noise. The framework achieves a favorable trade-off among interpretability, robustness, and computational efficiency. Empirically, it attains state-of-the-art or near-state-of-the-art performance on standard heterophilous benchmarks—including Cornell, Texas, and Wisconsin—demonstrating its capability to effectively model higher-order structural dependencies.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Reasoning under Uncertainty: Graphical Models

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Addresses GNN struggles with heterophilous graphs
• Improves neighbor aggregation and structural pattern incorporation
• Enhances interpretability and robustness in graph learning

Innovation

• Logic-guided reasoning for structured embeddings
• Dynamic graph refinement via attention-based pruning
• Adaptive clustering captures global patterns