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However, they generally connect the nodes with the same content between subgraphs directly to frame a larger graph network, which has serious sparsity in connections, especially when subgraphs possess limited semantics. To this end, we propose tight-fitting graph inference network (TFGIN), which innovatively builds tight-fitting graphs (TF-graphs) to strengthen the connections of subgraphs and designs inference modeling layer (IML) to learn coherence evidence for fact verification. Specifically, different from traditional connection ways, the constructed TF-graph enhances inter-graph and intra-graph connections of subgraphs through subgraph segmentation and interaction guidance mechanisms. IML could reason the semantics with strong correlation and high consistency as explainable evidence. Experiments on three competitive datasets confirm the superiority and scalability of our TFGIN.<\/jats:p>","DOI":"10.1145\/3734520","type":"journal-article","created":{"date-parts":[[2025,5,6]],"date-time":"2025-05-06T11:56:09Z","timestamp":1746532569000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":31,"title":["TFGIN: Tight-Fitting Graph Inference Network for Table-based Fact Verification"],"prefix":"10.1145","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1451-9295","authenticated-orcid":false,"given":"Lianwei","family":"Wu","sequence":"first","affiliation":[{"name":"School of Computer Science, Northwestern Polytechnical University, Xi\u2019an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8134-4873","authenticated-orcid":false,"given":"Kang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Northwestern Polytechnical University, Xi\u2019an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8502-8566","authenticated-orcid":false,"given":"Kunlin","family":"Nie","sequence":"additional","affiliation":[{"name":"The 15th Research Institute of China Electronics Technology Group Corporation, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9340-1521","authenticated-orcid":false,"given":"Sensen","family":"Guo","sequence":"additional","affiliation":[{"name":"Xidian University, Xi\u2019an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5406-9730","authenticated-orcid":false,"given":"Chao","family":"Gao","sequence":"additional","affiliation":[{"name":"Northwestern Polytechnical University, Xi\u2019an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6788-1357","authenticated-orcid":false,"given":"Zhen","family":"Wang","sequence":"additional","affiliation":[{"name":"Northwestern Polytechnical University, Xi\u2019an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6381-1984","authenticated-orcid":false,"given":"Shudong","family":"Li","sequence":"additional","affiliation":[{"name":"The Cyberspace Institute of Advanced Technology, Guangdong Key Laboratory of Industrial Control System Security, and Huangpu Research School, Guangzhou University, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,7,21]]},"reference":[{"key":"e_1_3_2_2_2","volume-title":"Annual Conference on Neural Information Processing Systems","author":"Aly Rami","year":"2021","unstructured":"Rami Aly, Zhijiang Guo, Michael Schlichtkrull, James Thorne, Andreas Vlachos, Christos Christodoulopoulos, Oana Cocarascu, and Arpit Mittal. 2021. 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