When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty

• URL: http://arxiv.org/abs/2505.13989v2
• Date: Wed, 21 May 2025 04:23:56 GMT
• Title: When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty
• Authors: Yanzhe Wen, Xunkai Li, Qi Zhang, Zhu Lei, Guang Zeng, Rong-Hua Li, Guoren Wang,
• Abstract summary: Large language models (LLMs) have significantly advanced text-attributed graph (TAG) learning.<n>Open-world Graph Assistant (OGA) is a framework that combines adaptive label traceability, which integrates semantics and topology for unknown-class rejection.<n> Comprehensive experiments demonstrate OGA's effectiveness and practicality.
• Score: 27.91526923088602
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recently, large language models (LLMs) have significantly advanced text-attributed graph (TAG) learning. However, existing methods inadequately handle data uncertainty in open-world scenarios, especially concerning limited labeling and unknown-class nodes. Prior solutions typically rely on isolated semantic or structural approaches for unknown-class rejection, lacking effective annotation pipelines. To address these limitations, we propose Open-world Graph Assistant (OGA), an LLM-based framework that combines adaptive label traceability, which integrates semantics and topology for unknown-class rejection, and a graph label annotator to enable model updates using newly annotated nodes. Comprehensive experiments demonstrate OGA's effectiveness and practicality.

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