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.

本研究通过语言监督来改善视图生成，介绍了 GAugLLM 框架，利用先进的大型语言模型（如 Mistral）增强自监督图学习，通过混合提示专家技术生成增强的节点特征，以及协作边修饰器来增强边的生成。实验结果表明，该框架可以作为插件工具增强领先的对比方法的性能，同时也能增强标准生成方法和流行的图神经网络的性能。