{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T05:25:53Z","timestamp":1766726753643,"version":"3.48.0"},"reference-count":49,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T00:00:00Z","timestamp":1766534400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Research Project of Guangdong Provincial Administration of Traditional Chinese Medicine","award":["20242047"],"award-info":[{"award-number":["20242047"]}]},{"name":"Teaching Quality Enhancement Project of Guangdong Pharmaceutical University","award":["2022"],"award-info":[{"award-number":["2022"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62407016"],"award-info":[{"award-number":["62407016"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Community detection in social networks is one of the most important topics of network science. Researchers have developed numerous methods from various perspectives. However, the existing methods often overlook the team information encoded as a special type of user relation in the social network, which plays an important role in community formation and evolution. In this paper, we propose a novel community detection algorithm called Team-aware Community Detection (TaCD). Our model constructs a multi-view network by encoding the user interaction information as the user view and the team information as the team view. To measure the consistency across the two views, we use the Jaccard similarity to establish a cross-view coupling. Based on the constructed 2-view network, we use multi-view modularity to discover team-aware community structure, and solve the optimization problem using the well-known Generalized Louvain approach. Another contribution of this paper is the collection of a new SCHOLAT dataset, which consists of several social networks with team information and is publicly available for testing purposes. Our experimental results on several SCHOLAT networks with team information demonstrate that TaCD outperforms the existing community detection algorithms.<\/jats:p>","DOI":"10.3390\/e28010021","type":"journal-article","created":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T12:22:27Z","timestamp":1766578947000},"page":"21","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["TaCD: Team-Aware Community Detection Based on Multi-View Modularity"],"prefix":"10.3390","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1167-9755","authenticated-orcid":false,"given":"Chengzhou","family":"Fu","sequence":"first","affiliation":[{"name":"College of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou 510006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feiyi","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Guangzhou Polytechnic University, Guangzhou 511483, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lingzhi","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Modern Information Industry, Guangzhou College of Commerce, Guangzhou 511363, China"},{"name":"Faculty of Applied Sciences, Macao Polytechnic University, Macau 999078, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengzhe","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Electronics and Information, Guangdong Polytechnic Normal University, Guangzhou 510665, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ronghua","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Computer Science, South China Normal University, Guangzhou 510631, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5135","DOI":"10.1007\/s00521-020-05311-w","article-title":"A local-to-global scheme-based multi-objective evolutionary algorithm for overlapping community detection on large-scale complex networks","volume":"33","author":"Ma","year":"2020","journal-title":"Neural Comput. 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