<ul><li>Researchers are exploring the integration of language descriptions into graphs, known as text-attributed graphs (TAGs), to enhance model encoding capabilities.</li><li>Graph structure learning (GSL) is a crucial technique for improving data utility, and it is highly relevant to efficient TAG learning.</li><li>The challenge is to define a reasonable optimization objective for GSL in the era of large language models (LLMs) and design an efficient model architecture for LLM integration.</li><li>The proposed Large Language and Tree Assistant (LLaTA) leverages tree-based LLM in-context learning to enhance the understanding of topology and text in order to generate improved graph structures.</li></ul>

Rethinking Graph Structure Learning in the Era of LLMs

Discover more