Enhancing Spectral Graph Neural Networks with LLM-Predicted Homophily

• URL: http://arxiv.org/abs/2506.14220v2
• Date: Tue, 05 Aug 2025 02:35:26 GMT
• Title: Enhancing Spectral Graph Neural Networks with LLM-Predicted Homophily
• Authors: Kangkang Lu, Yanhua Yu, Zhiyong Huang, Tat-Seng Chua,
• Abstract summary: Spectral Graph Neural Networks (SGNNs) have achieved remarkable performance in tasks such as node classification.<n>We propose a novel framework that leverages Large Language Models (LLMs) to estimate the homophily level of a graph.<n>Our framework consistently improves performance over strong SGNN baselines.
• Score: 48.135717446964385
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spectral Graph Neural Networks (SGNNs) have achieved remarkable performance in tasks such as node classification due to their ability to learn flexible filters. Typically, these filters are learned under the supervision of downstream tasks, enabling SGNNs to adapt to diverse structural patterns. However, in scenarios with limited labeled data, SGNNs often struggle to capture the optimal filter shapes, resulting in degraded performance, especially on graphs with heterophily. Meanwhile, the rapid progress of Large Language Models (LLMs) has opened new possibilities for enhancing graph learning without modifying graph structure or requiring task-specific training. In this work, we propose a novel framework that leverages LLMs to estimate the homophily level of a graph and uses this global structural prior to guide the construction of spectral filters. Specifically, we design a lightweight and plug-and-play pipeline where a small set of labeled node pairs is formatted as natural language prompts for the LLM, which then predicts the graph's homophily ratio. This estimated value informs the spectral filter basis, enabling SGNNs to adapt more effectively to both homophilic and heterophilic structures. Extensive experiments on multiple benchmark datasets demonstrate that our LLM-assisted spectral framework consistently improves performance over strong SGNN baselines. Importantly, this enhancement incurs negligible computational and monetary cost, making it a practical solution for real-world graph applications.

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