GraspLLM: 面向文本属性图与LLM的零样本泛化
GraspLLM: Towards Zero-Shot Generalization on Text-Attributed Graphs with LLMs
AI总结：
提出GraspLLM框架，通过融合图结构理解与LLM语义能力，利用基序感知对比学习和最优上下文子图对齐，实现跨数据集和跨任务的零样本泛化。
摘要：
近年来，对文本属性图（TAGs）的研究因其在引文网络、电子商务平台、社交媒体和网页等各类真实数据场景中的广泛应用而备受关注。受大语言模型（LLMs）卓越语义理解能力的启发，已有许多尝试将LLMs集成到TAGs中。然而，现有方法仍难以在不同图和任务间泛化，且其捕获可迁移图结构模式的能力有限。为此，我们提出了GraspLLM框架，该框架将图结构理解与LLM的语义理解能力相结合，以增强跨数据集和跨任务的泛化能力。具体而言，我们使用冻结的通用嵌入模型将不同图的节点文本表示在统一语义空间中，在此基础上，我们在多个基序诱导的邻接矩阵上进行基序感知对比学习，以提取与数据集无关的结构信息。然后，通过我们提出的最优上下文子图，为每个目标节点提取最相关的上下文子图，并通过对齐投影仪将这些子图对齐到LLM的令牌空间。在涵盖不同领域的TAG基准数据集上的大量实验表明，GraspLLM在零样本场景下始终优于先前基于LLM的TAG方法，突显了其在不同数据集和任务上的强泛化能力。我们的代码可在以下网址获取：此 https URL。
英文摘要：
Research on Text-Attributed Graphs (TAGs) has gained significant attention recently due to its broad applications across various real-world data scenarios, such as citation networks, e-commerce platforms, social media, and web pages. Inspired by the remarkable semantic understanding ability of Large Language Models (LLMs), there have been numerous attempts to integrate LLMs into TAGs. However, existing methods still struggle to generalize across diverse graphs and tasks, and their ability to capture transferable graph structural patterns remains limited. To address this, we introduce the GraspLLM, a framework that combines Graph structural comprehension with semantic understanding prowess of LLMs to enhance the cross-dataset and cross-task generalizability. Specifically, we represent node texts from different graphs in a unified semantic space with a frozen general embedding model, on top of which we perform motif-aware contrastive learning across multiple motif-induced adjacency matrices to extract dataset-agnostic structural information. Then, with our proposed optimal contextual subgraph, we extract the most contextually relevant subgraph for each target node and align these subgraphs to the token space of LLM via an alignment projector. Extensive experiments on TAG benchmark datasets spanning diverse domains reveal that GraspLLM consistently outperforms previous LLM-based methods for TAGs, especially in zero-shot scenarios, highlighting its strong generalizability across different datasets and tasks. Our code is available at https://github.com/Heinz217/GraspLLM.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
