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arXiv · 2505.13989

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

Yanzhe Wen·Xunkai Li·Qi Zhang·Zhu Lei·Guang Zeng·Rong-Hua Li·Guoren Wang
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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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Yanzhe Wen, Xunkai Li, Qi Zhang, Zhu Lei, Guang Zeng, Rong-Hua Li, Guoren Wang. 2025-05-20. When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty. https://arxiv.org/abs/2505.13989

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