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

• URL: http://arxiv.org/abs/2507.18521v1
• Date: Thu, 24 Jul 2025 15:45:26 GMT
• Title: GLANCE: Graph Logic Attention Network with Cluster Enhancement for Heterophilous Graph Representation Learning
• Authors: Zhongtian Sun, Anoushka Harit, Alexandra Cristea, Christl A. Donnelly, Pietro Liò,
• Abstract summary: Graph Neural Networks (GNNs) have demonstrated significant success in learning from graph-structured data but often struggle on heterophilous graphs.<n>We propose GLANCE, a novel framework that integrates logic-guided reasoning, dynamic graph refinement, and adaptive clustering to enhance graph representation learning.
• Score: 54.60090631330295
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• Unsupervised Graph Clustering with Deep Structural Entropy [25.38926876388394]
  DeSE is a novel unsupervised graph clustering framework incorporating Deep Structural Entropy.<n>It enhances the original graph with quantified structural information and deep neural networks to form clusters.<n>Our clustering assignment method learns node embeddings and a soft assignment matrix to cluster on the enhanced graph.
  arXiv  Detail & Related papers  (2025-05-20T07:38:06Z)
• Revisiting Graph Neural Networks on Graph-level Tasks: Comprehensive Experiments, Analysis, and Improvements [54.006506479865344]
  We propose a unified evaluation framework for graph-level Graph Neural Networks (GNNs)<n>This framework provides a standardized setting to evaluate GNNs across diverse datasets.<n>We also propose a novel GNN model with enhanced expressivity and generalization capabilities.
  arXiv  Detail & Related papers  (2025-01-01T08:48:53Z)
• Spectral Greedy Coresets for Graph Neural Networks [61.24300262316091]
  The ubiquity of large-scale graphs in node-classification tasks hinders the real-world applications of Graph Neural Networks (GNNs) This paper studies graph coresets for GNNs and avoids the interdependence issue by selecting ego-graphs based on their spectral embeddings. Our spectral greedy graph coreset (SGGC) scales to graphs with millions of nodes, obviates the need for model pre-training, and applies to low-homophily graphs.
  arXiv  Detail & Related papers  (2024-05-27T17:52:12Z)
• Deep Contrastive Graph Learning with Clustering-Oriented Guidance [61.103996105756394]
  Graph Convolutional Network (GCN) has exhibited remarkable potential in improving graph-based clustering. Models estimate an initial graph beforehand to apply GCN. Deep Contrastive Graph Learning (DCGL) model is proposed for general data clustering.
  arXiv  Detail & Related papers  (2024-02-25T07:03:37Z)
• GraphEdit: Large Language Models for Graph Structure Learning [14.16155596597421]
  Graph Structure Learning (GSL) focuses on capturing intrinsic dependencies and interactions among nodes in graph-structured data.<n>Existing GSL methods heavily depend on explicit graph structural information as supervision signals.<n>We propose GraphEdit, an approach that leverages large language models (LLMs) to learn complex node relationships in graph-structured data.
  arXiv  Detail & Related papers  (2024-02-23T08:29:42Z)
• Transforming Graphs for Enhanced Attribute Clustering: An Innovative Graph Transformer-Based Method [8.989218350080844]
  This study introduces an innovative method known as the Graph Transformer Auto-Encoder for Graph Clustering (GTAGC) By melding the Graph Auto-Encoder with the Graph Transformer, GTAGC is adept at capturing global dependencies between nodes. The architecture of GTAGC encompasses graph embedding, integration of the Graph Transformer within the autoencoder structure, and a clustering component.
  arXiv  Detail & Related papers  (2023-06-20T06:04:03Z)
• Towards Unsupervised Deep Graph Structure Learning [67.58720734177325]
  We propose an unsupervised graph structure learning paradigm, where the learned graph topology is optimized by data itself without any external guidance. Specifically, we generate a learning target from the original data as an "anchor graph", and use a contrastive loss to maximize the agreement between the anchor graph and the learned graph.
  arXiv  Detail & Related papers  (2022-01-17T11:57:29Z)
• Graph Clustering with Graph Neural Networks [5.305362965553278]
  Graph Neural Networks (GNNs) have achieved state-of-the-art results on many graph analysis tasks. Unsupervised problems on graphs, such as graph clustering, have proved more resistant to advances in GNNs. We introduce Deep Modularity Networks (DMoN), an unsupervised pooling method inspired by the modularity measure of clustering quality.
  arXiv  Detail & Related papers  (2020-06-30T15:30:49Z)
• Adaptive Graph Auto-Encoder for General Data Clustering [90.8576971748142]
  Graph-based clustering plays an important role in the clustering area. Recent studies about graph convolution neural networks have achieved impressive success on graph type data. We propose a graph auto-encoder for general data clustering, which constructs the graph adaptively according to the generative perspective of graphs.
  arXiv  Detail & Related papers  (2020-02-20T10:11:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.