{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T16:55:46Z","timestamp":1773420946281,"version":"3.50.1"},"reference-count":44,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T00:00:00Z","timestamp":1773360000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Green Willow Finch Plan"},{"name":"Foundation of the Jiangsu Province High Vocational College Teacher Professional Leader High-end Training Program","award":["62023TDFX012"],"award-info":[{"award-number":["62023TDFX012"]}]},{"name":"Wenzhou-Kean University internal research","award":["ISRG2024008"],"award-info":[{"award-number":["ISRG2024008"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>Bipartite network community detection is of significant importance for understanding the underlying structure and functional organization of real-world complex systems. Although many mature community detection algorithms exist for unipartite networks, they cannot be directly applied to bipartite networks due to their unique topological structure, characterized by heterogeneous node types and cross-layer connections. Furthermore, some existing bipartite network community detection methods still rely heavily on manual experience to set key parameters, which limits their applicability and scalability in practical scenarios. To address these issues, this paper proposes an enhanced framework\u2014the Adaptive Network Reconstruction Framework (ANRF)\u2014by introducing an adaptive parameter optimization mechanism based on the existing Network Reconstruction Framework (NRF). This framework can be effectively integrated with traditional unipartite network community detection algorithms to achieve automatic community detection with reduced dependence on manual parameter tuning. The core procedure of the method consists of four main steps. First, we calculate the interaction forces between node pairs. Second, through comprehensive analysis of the network topological features, we adaptively determine the threshold parameter \u03b8 and related parameters for the interaction forces. Third, based on these thresholds and parameters, we perform edge filtering on the bipartite network to construct a reconstructed network. Finally, we apply unipartite community detection algorithms directly to the reconstructed network to obtain the community structure. To validate the effectiveness of ANRF, we combined it with the Louvain method and the Greedy modularity method, and conducted experimental evaluations on multiple synthetic and real-world network datasets. A systematic comparison with current state-of-the-art algorithms was made. The experimental results on multiple synthetic and real-world datasets within our evaluated scope demonstrate that ANRF achieves competitive performance in terms of community modularity and community density compared to state-of-the-art algorithms, while significantly reducing reliance on manual parameter tuning and enhancing robustness under the tested conditions.<\/jats:p>","DOI":"10.3390\/fi18030147","type":"journal-article","created":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T15:06:22Z","timestamp":1773414382000},"page":"147","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ANRF: An Adaptive Network Reconstruction Framework for Community Detection in Bipartite Networks"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1558-4120","authenticated-orcid":false,"given":"Furong","family":"Chang","sequence":"first","affiliation":[{"name":"School of Information Engineering, Yangzhou Polytechnic Institute, Yangzhou 225127, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Songxian","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Foundation Sciences, Yangzhou Polytechnic Institute, Yangzhou 225127, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7865-5555","authenticated-orcid":false,"given":"Yue","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Science, Mathematics and Technology, Wenzhou-Kean University, Wenzhou 325060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Farhan","family":"Ullah","sequence":"additional","affiliation":[{"name":"Cybersecurity Center, Prince Mohammad Bin Fahd University, Al Khobar 34754, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,13]]},"reference":[{"key":"ref_1","unstructured":"Alzahrani, T. 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