{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T11:40:59Z","timestamp":1774352459074,"version":"3.50.1"},"reference-count":16,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"vor","delay-in-days":59,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100013139","name":"Humanities and Social Science Fund of Ministry of Education of China","doi-asserted-by":"publisher","award":["24YJCZH079"],"award-info":[{"award-number":["24YJCZH079"]}],"id":[{"id":"10.13039\/501100013139","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012456","name":"National Social Science Fund of China","doi-asserted-by":"publisher","award":["22CTQ026"],"award-info":[{"award-number":["22CTQ026"]}],"id":[{"id":"10.13039\/501100012456","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:p>The dynamic and ever\u2010evolving nature of Internet\u2010generated temporal networks poses significant challenges for traditional network analysis methods, which often overlook the rich temporal information embedded within the data. This oversight can lead to an incomplete understanding of the network\u2019s structure and its evolutionary patterns over time. To tackle this problem, this paper introduces an algorithm designed to uncover tightly knit communities within short time spans by leveraging the comprehensive information contained in time\u2010series data. Our method employs a computational approach that slices the temporal network into meaningful segments, enabling the identification of transient yet highly cohesive communities. Furthermore, it gauges the level of cohesion within these communities, providing analysts with a valuable tool for understanding the network\u2019s dynamic behavior. Through experimentation, we demonstrate the effectiveness of this algorithm in accurately capturing the evolving structures of temporal networks, thereby contributing to a deeper comprehension of complex network dynamics.<\/jats:p>","DOI":"10.1155\/cplx\/5604982","type":"journal-article","created":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T11:55:16Z","timestamp":1772970916000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhanced Short\u2010Term Identification of Robust Communities Leveraging User Popularity and Engagement Analytics"],"prefix":"10.1155","volume":"2026","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7069-0367","authenticated-orcid":false,"given":"Lin","family":"Guo","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8009-275X","authenticated-orcid":false,"given":"Ru","family":"Yi","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2026,3,1]]},"reference":[{"key":"e_1_2_15_1_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0250612"},{"key":"e_1_2_15_2_2","doi-asserted-by":"publisher","DOI":"10.1103\/physrevresearch.2.023073"},{"key":"e_1_2_15_3_2","doi-asserted-by":"publisher","DOI":"10.1038\/srep00469"},{"key":"e_1_2_15_4_2","doi-asserted-by":"publisher","DOI":"10.1002\/net.22034"},{"key":"e_1_2_15_5_2","doi-asserted-by":"crossref","unstructured":"VeldtN. BensonA. R. andKleinbergJ. The Generalized Mean Densest Subgraph Problem The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining 2021 Singapore.","DOI":"10.1145\/3447548.3467398"},{"key":"e_1_2_15_6_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-43029-5"},{"key":"e_1_2_15_7_2","doi-asserted-by":"crossref","unstructured":"ChuL. ZhangY. YangY.et al. Online Density Bursting Subgraph Detection From Temporal Graphs Proceedings of the VLDB Endowment 2019 Los Angeles CA.","DOI":"10.14778\/3358701.3358704"},{"key":"e_1_2_15_8_2","doi-asserted-by":"crossref","unstructured":"ZhangQ. GuoD. ZhaoX.et al. Seasonal-Periodic Subgraph Mining in Temporal Networks The 29th ACM International Conference on Information and Knowledge Management 2020 Galway Ireland.","DOI":"10.1145\/3340531.3412091"},{"key":"e_1_2_15_9_2","doi-asserted-by":"publisher","DOI":"10.1017\/nws.2020.38"},{"key":"e_1_2_15_10_2","doi-asserted-by":"crossref","unstructured":"QinH. LiR. H. WangG.et al. Mining Periodic Cliques in Temporal Networks Proceedings of the 2019 IEEE 35th International Conference on Data Engineering 2019 Macao China 1130\u20131141.","DOI":"10.1109\/ICDE.2019.00104"},{"key":"e_1_2_15_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2020.2985295"},{"key":"e_1_2_15_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipl.2018.10.016"},{"key":"e_1_2_15_13_2","unstructured":"ShuaiM. HuR. WangL.et al. Fast Computation of Dense Temporal Subgraphs[C] 2017 IEEE 33rd International Conference on Data Engineering (ICDE) 2017 San Diego CA IEEE."},{"key":"e_1_2_15_14_2","article-title":"Fast Unfolding of Communities in Large Networks","volume":"10","author":"Blondel V. D.","year":"2008","journal-title":"Journal of Statistical Mechanics: Theory and Experiment"},{"key":"e_1_2_15_15_2","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/6688750"},{"key":"e_1_2_15_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2016.08.012"}],"container-title":["Complexity"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/cplx\/5604982","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1155\/cplx\/5604982","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/cplx\/5604982","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T09:51:52Z","timestamp":1774345912000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/cplx\/5604982"}},"subtitle":[],"editor":[{"given":"Naoki","family":"Masuda","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2026,1]]},"references-count":16,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["10.1155\/cplx\/5604982"],"URL":"https:\/\/doi.org\/10.1155\/cplx\/5604982","archive":["Portico"],"relation":{},"ISSN":["1076-2787","1099-0526"],"issn-type":[{"value":"1076-2787","type":"print"},{"value":"1099-0526","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1]]},"assertion":[{"value":"2025-05-26","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-01-03","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-03-01","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"5604982"}}