GAugLLM: Improving Graph Contrastive Learning for Text-Attributed Graphs with Large Language Models

TL;DR

GAugLLM introduces a novel framework that leverages large language models and prompt engineering to improve view generation in self-supervised graph learning for text-attributed graphs, enhancing contrastive learning performance.

Contribution

It proposes a mixture-of-prompt-expert technique and a collaborative edge modifier to better augment textual node features and graph structure using LLMs, addressing challenges of text variability and alignment.

Findings

Enhanced contrastive learning performance across five benchmark datasets.

Augmented features and structures improve standard generative and GNN methods.

Open-source implementation available for reproducibility.

Abstract

This work studies self-supervised graph learning for text-attributed graphs (TAGs) where nodes are represented by textual attributes. Unlike traditional graph contrastive methods that perturb the numerical feature space and alter the graph's topological structure, we aim to improve view generation through language supervision. This is driven by the prevalence of textual attributes in real applications, which complement graph structures with rich semantic information. However, this presents challenges because of two major reasons. First, text attributes often vary in length and quality, making it difficulty to perturb raw text descriptions without altering their original semantic meanings. Second, although text attributes complement graph structures, they are not inherently well-aligned. To bridge the gap, we introduce GAugLLM, a novel framework for augmenting TAGs. It leverages advanced large language models like Mistral to enhance self-supervised graph learning. Specifically, we introduce a mixture-of-prompt-expert technique to generate augmented node features. This approach adaptively maps multiple prompt experts, each of which modifies raw text attributes using prompt engineering, into numerical feature space. Additionally, we devise a collaborative edge modifier to leverage structural and textual commonalities, enhancing edge augmentation by examining or building connections between nodes. Empirical results across five benchmark datasets spanning various domains underscore our framework's ability to enhance the performance of leading contrastive methods as a plug-in tool. Notably, we observe that the augmented features and graph structure can also enhance the performance of standard generative methods, as well as popular graph neural networks. The open-sourced implementation of our GAugLLM is available at Github.