GAugLLM: 利用大语言模型改进文本属性图的图对比学习
GAugLLM: Improving Graph Contrastive Learning for Text-Attributed Graphs with Large Language Models
AI总结：
针对文本属性图自监督学习中文本扰动难、图文不对齐的问题，提出GAugLLM框架，通过混合提示专家和协同边修改器实现增强，可即插即用提升多种图学习方法性能。
摘要：
本研究针对节点以文本属性表示的文本属性图（TAGs），开展自监督图学习研究。传统图对比方法会扰动数值特征空间并改变图的拓扑结构，而本研究旨在通过语言监督改进视图生成。这一研究思路的动因是实际应用中文本属性普遍存在，其丰富的语义信息可对图结构形成补充。但该方向存在两大挑战：其一，文本属性的长度和质量往往参差不齐，难以在不改变原始语义的前提下扰动原始文本描述；其二，文本属性虽能补充图结构，但二者本质上并未实现良好对齐。\n为解决上述问题，我们提出了一种新型TAG增强框架GAugLLM。该框架借助Mistral等先进大语言模型，提升自监督图学习效果。具体而言，我们提出了混合提示专家技术，用于生成增强后的节点特征：该方法包含多个提示专家，每个专家通过提示工程修改原始文本属性，框架会将这些提示专家自适应映射到数值特征空间。此外，我们设计了协同边修改器，利用结构与文本的共性，通过检查或构建节点间连接来增强边增强效果。\n在跨多个领域的5个基准数据集上的实验结果表明，本框架可作为即插即用工具，提升主流对比方法的性能。值得注意的是，增强后的特征与图结构还能提升标准生成方法以及主流图神经网络的性能。GAugLLM的开源实现已在Github发布。
英文摘要：
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.
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