TAGFN: A Text-Attributed Graph Dataset for Fake News Detection in the Age of LLMs
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911288576180224 |
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| author | Liu, Kay Han, Yuwei Xu, Haoyan Zou, Henry Peng Zhao, Yue Yu, Philip S. |
| author_facet | Liu, Kay Han, Yuwei Xu, Haoyan Zou, Henry Peng Zhao, Yue Yu, Philip S. |
| contents | Large Language Models (LLMs) have recently revolutionized machine learning on text-attributed graphs, but the application of LLMs to graph outlier detection, particularly in the context of fake news detection, remains significantly underexplored. One of the key challenges is the scarcity of large-scale, realistic, and well-annotated datasets that can serve as reliable benchmarks for outlier detection. To bridge this gap, we introduce TAGFN, a large-scale, real-world text-attributed graph dataset for outlier detection, specifically fake news detection. TAGFN enables rigorous evaluation of both traditional and LLM-based graph outlier detection methods. Furthermore, it facilitates the development of misinformation detection capabilities in LLMs through fine-tuning. We anticipate that TAGFN will be a valuable resource for the community, fostering progress in robust graph-based outlier detection and trustworthy AI. The dataset is publicly available at https://huggingface.co/datasets/kayzliu/TAGFN and our code is available at https://github.com/kayzliu/tagfn. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_21624 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TAGFN: A Text-Attributed Graph Dataset for Fake News Detection in the Age of LLMs Liu, Kay Han, Yuwei Xu, Haoyan Zou, Henry Peng Zhao, Yue Yu, Philip S. Social and Information Networks Computation and Language Large Language Models (LLMs) have recently revolutionized machine learning on text-attributed graphs, but the application of LLMs to graph outlier detection, particularly in the context of fake news detection, remains significantly underexplored. One of the key challenges is the scarcity of large-scale, realistic, and well-annotated datasets that can serve as reliable benchmarks for outlier detection. To bridge this gap, we introduce TAGFN, a large-scale, real-world text-attributed graph dataset for outlier detection, specifically fake news detection. TAGFN enables rigorous evaluation of both traditional and LLM-based graph outlier detection methods. Furthermore, it facilitates the development of misinformation detection capabilities in LLMs through fine-tuning. We anticipate that TAGFN will be a valuable resource for the community, fostering progress in robust graph-based outlier detection and trustworthy AI. The dataset is publicly available at https://huggingface.co/datasets/kayzliu/TAGFN and our code is available at https://github.com/kayzliu/tagfn. |
| title | TAGFN: A Text-Attributed Graph Dataset for Fake News Detection in the Age of LLMs |
| topic | Social and Information Networks Computation and Language |
| url | https://arxiv.org/abs/2511.21624 |