{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T05:06:15Z","timestamp":1773464775146,"version":"3.50.1"},"reference-count":28,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2020,11,13]],"date-time":"2020-11-13T00:00:00Z","timestamp":1605225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2021,2,28]]},"abstract":"<jats:p>We investigate the problem of community detection in bipartite networks that are characterized by the presence of two types of nodes such that connections exist only between nodes of different types. While some approaches have been proposed to identify community structures in bipartite networks, there are a number of problems still to solve. In fact, the majority of the proposed approaches suffer from one or even more of the following limitations: (1) difficulty in detecting communities in the presence of many non-discriminating nodes with atypical connections that hide the community structures, (2) loss of relevant topological information due to the transformation of the bipartite network to standard plain graphs, and (3) manually specifying several input parameters, including the number of communities to be identified. To alleviate these problems, we propose BiNeTClus, a parameter-free community detection algorithm in bipartite networks that operates in two phases. The first phase focuses on identifying an initial grouping of nodes through a transactional data model capable of dealing with the situation that involves networks with many atypical connections, that is, sparsely connected nodes and nodes of one type that massively connect to all other nodes of the second type. The second phase aims to refine the clustering results of the first phase via an optimization strategy of the bipartite modularity to identify the final community structures. Our experiments on both synthetic and real networks illustrate the suitability of the proposed approach.<\/jats:p>","DOI":"10.1145\/3423067","type":"journal-article","created":{"date-parts":[[2020,11,24]],"date-time":"2020-11-24T21:47:31Z","timestamp":1606254451000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["BiNeTClus"],"prefix":"10.1145","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0851-8889","authenticated-orcid":false,"given":"Mohamed","family":"Bouguessa","sequence":"first","affiliation":[{"name":"University of Quebec at Montreal, Montreal, Canada"}]},{"given":"Khaled","family":"Nouri","sequence":"additional","affiliation":[{"name":"University of Quebec at Montreal, Montreal, Canada"}]}],"member":"320","published-online":{"date-parts":[[2020,11,13]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1961189.1961194"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2898361"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1088\/1742-5468\/2008\/10\/P10008"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2594454"},{"key":"e_1_2_1_5_1","volume-title":"Complex Systems and Networks","author":"Alzahrani Taher","unstructured":"Taher Alzahrani and Kathy Horadam . 2016. Community detection in bipartite networks: Algorithms and case studies . In Complex Systems and Networks . Springer , 25--50. Taher Alzahrani and Kathy Horadam. 2016. Community detection in bipartite networks: Algorithms and case studies. In Complex Systems and Networks. Springer, 25--50."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0097823"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.69.066133"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/11569596_31"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.76.036106"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.76.066102"},{"key":"e_1_2_1_11_1","first-page":"184","article-title":"Community detection in large-scale bipartite networks","volume":"5","author":"Liu Xin","year":"2010","unstructured":"Xin Liu and Tsuyoshi Murata . 2010 . Community detection in large-scale bipartite networks . Inf. Media Technol. 5 , 1 (2010), 184 -- 192 . Xin Liu and Tsuyoshi Murata. 2010. Community detection in large-scale bipartite networks. Inf. Media Technol. 5, 1 (2010), 184--192.","journal-title":"Inf. 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J. Richard Shi , and Hongxia Yang . 2019 . Hierarchical representation learning for bipartite graphs . In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI\u201919) . 2873--2879. Li Chong, Kunyang Jia, Dan Shen, C. J. Richard Shi, and Hongxia Yang. 2019. Hierarchical representation learning for bipartite graphs. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI\u201919). 2873--2879."},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313493"},{"key":"e_1_2_1_28_1","volume-title":"Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM\u201917)","author":"Yao Zhang","year":"2017","unstructured":"Zhang Yao , Yun Xiong , Xiangnan Kong , and Yangyong Zhu . 2017 . Learning node embeddings in interaction graphs . In Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM\u201917) . 397--406. 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In Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM\u201917). 397--406."}],"container-title":["ACM Transactions on Intelligent Systems and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3423067","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3423067","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T21:24:56Z","timestamp":1750195496000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3423067"}},"subtitle":["Bipartite Network Community Detection Based on Transactional Clustering"],"short-title":[],"issued":{"date-parts":[[2020,11,13]]},"references-count":28,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,2,28]]}},"alternative-id":["10.1145\/3423067"],"URL":"https:\/\/doi.org\/10.1145\/3423067","relation":{},"ISSN":["2157-6904","2157-6912"],"issn-type":[{"value":"2157-6904","type":"print"},{"value":"2157-6912","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,13]]},"assertion":[{"value":"2019-10-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-09-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-11-13","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}