GraspLLM: Towards Zero-Shot Generalization on Text-Attributed Graphs with LLMs
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...
| Authors | Hengyi Feng, Zeang Sheng, Meiyi Qiang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 10 June 2026 |
| Last updated at source | 11 June 2026 |
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