Enhancing Spectral Graph Neural Networks with LLM-Predicted Homophily

TL;DR

This paper introduces a novel LLM-assisted framework that estimates graph homophily to guide spectral filter construction in SGNNs, improving performance especially on heterophilic graphs with minimal additional cost.

Contribution

The work presents a lightweight, plug-and-play method using LLMs to estimate graph homophily and enhance spectral graph neural networks without modifying graph structure or extensive training.

Findings

Consistent performance improvements on benchmark datasets.

Effective adaptation to both homophilic and heterophilic graphs.

Negligible additional computational and monetary cost.

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.