When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty
Fuente:
arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912384938934272 |
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| author | Wen, Yanzhe Li, Xunkai Zhang, Qi Lei, Zhu Zeng, Guang Li, Rong-Hua Wang, Guoren |
| author_facet | Wen, Yanzhe Li, Xunkai Zhang, Qi Lei, Zhu Zeng, Guang Li, Rong-Hua Wang, Guoren |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_13989 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty Wen, Yanzhe Li, Xunkai Zhang, Qi Lei, Zhu Zeng, Guang Li, Rong-Hua Wang, Guoren Machine Learning Artificial Intelligence 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. |
| title | When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2505.13989 |