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

arXiv: https://arxiv.org/abs/2507.18521 · HTML: https://mlsanity.com/paper/2507.18521/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 30 references that are in the map.
- First posted on arXiv 24 Jul 2025.

## Builds on (most cited first, 13 of 13)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Geom-GCN: Geometric Graph Convolutional Networks](https://mlsanity.com/paper/2002.05287/) (2020)
- [Deep Differentiable Logic Gate Networks](https://mlsanity.com/paper/2210.08277/) (2022)
- [Convolutional Differentiable Logic Gate Networks](https://mlsanity.com/paper/2411.04732/) (2024)
- [Robust Graph Structure Learning under Heterophily](https://mlsanity.com/paper/2403.03659/) (2024)
- [Revisiting the role of heterophily in graph representation learning: An edge classification perspective](https://mlsanity.com/paper/2205.11322/) (2022)
- [Breaking Down Financial News Impact: A Novel AI Approach with Geometric Hypergraphs](https://mlsanity.com/paper/2409.00438/) (2024)
- [Graph Contrastive Learning under Heterophily via Graph Filters](https://mlsanity.com/paper/2303.06344/) (2023)
- [Causal Spherical Hypergraph Networks for Modelling Social Uncertainty](https://mlsanity.com/paper/2506.17840/) (2025)
- [Actionable Interpretability via Causal Hypergraphs: Unravelling Batch Size Effects in Deep Learning](https://mlsanity.com/paper/2506.17826/) (2025)
- [ManifoldMind: Dynamic Hyperbolic Reasoning for Trustworthy Recommendations](https://mlsanity.com/paper/2507.02014/) (2025)

## Abstract (arXiv)

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.