{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T18:28:29Z","timestamp":1772303309909,"version":"3.50.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>Most real-world networks involved in big data applications are dynamic, making accurate identification of community structures crucial for optimizing and predicting the behavior of network individuals. When addressing the dynamic community detection, existing algorithms cannot simultaneously model the spatiotemporal patterns and node attentions appropriately, resulting in loss of detection accuracy. Motivated by the above issues, this paper innovatively presents a Modularity and Temporal proximity enhanced Nonnegative Tensor latent factorization (MTNT) method with three-fold ideas: a) Utilizing the nonnegative RESCAL framework for representing the dynamic evolution and potential community structure; b) Developing a modularity enhancement module to guarantee the spatial consistency between the detected communities and target network\u2019s intrinsic properties; c) Inventively introducing the node temporal proximity calculated by temporal personalized PageRank into the contrastive loss for significantly boosting the features\u2019 community semantics. Extensively experimental results obtained from six dynamic networks from real applications demonstrate that the MTNT is superior to state-of-the-art community detectors and the convergence of MTNT is verified.<\/jats:p>","DOI":"10.3233\/faia251052","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:50:05Z","timestamp":1761126605000},"source":"Crossref","is-referenced-by-count":1,"title":["Modularity and Temporal Proximity Enhanced Nonnegative Tensor Latent Factorization for Accurate Dynamic Community Detection"],"prefix":"10.3233","author":[{"given":"Hao","family":"Fang","sequence":"first","affiliation":[{"name":"Southwest University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Wu","sequence":"additional","affiliation":[{"name":"Southwest University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251052","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:50:05Z","timestamp":1761126605000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251052"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251052","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}