{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T05:28:32Z","timestamp":1767677312341,"version":"build-2065373602"},"reference-count":42,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,4,9]],"date-time":"2020-04-09T00:00:00Z","timestamp":1586390400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science and Technology Major Project of China","award":["2017ZX06002005"],"award-info":[{"award-number":["2017ZX06002005"]}]},{"name":"Science and Technology Research Project of the Science and Technology Department of Henan Province","award":["182102210472"],"award-info":[{"award-number":["182102210472"]}]},{"name":"Fundamental Research Funds for the Central Universities of Central South University","award":["2017zzts139"],"award-info":[{"award-number":["2017zzts139"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Identifying communities in dynamic networks is essential for exploring the latent network structures, understanding network functions, predicting network evolution, and discovering abnormal network events. Many dynamic community detection methods have been proposed from different viewpoints. However, identifying the community structure in dynamic networks is very challenging due to the difficulty of parameter tuning, high time complexity and detection accuracy decreasing as time slices increase. In this paper, we present a dynamic community detection framework based on information dynamics and develop a dynamic community detection algorithm called DCDID (dynamic community detection based on information dynamics), which uses a batch processing technique to incrementally uncover communities in dynamic networks. DCDID employs the information dynamics model to simulate the exchange of information among nodes and aims to improve the efficiency of community detection by filtering out the unchanged subgraph. To illustrate the effectiveness of DCDID, we extensively test it on synthetic and real-world dynamic networks, and the results demonstrate that the DCDID algorithm is superior to the representative methods in relation to the quality of dynamic community detection.<\/jats:p>","DOI":"10.3390\/e22040425","type":"journal-article","created":{"date-parts":[[2020,4,9]],"date-time":"2020-04-09T14:42:03Z","timestamp":1586443323000},"page":"425","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Identifying Communities in Dynamic Networks Using Information Dynamics"],"prefix":"10.3390","volume":"22","author":[{"given":"Zejun","family":"Sun","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Central South University, Changsha 401302, China"},{"name":"School of Information Engineering, Pingdingshan University, Pingdingshan 462500, China"}]},{"given":"Jinfang","family":"Sheng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University, Changsha 401302, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8815-7533","authenticated-orcid":false,"given":"Bin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University, Changsha 401302, China"}]},{"given":"Aman","family":"Ullah","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University, Changsha 401302, China"}]},{"given":"FaizaRiaz","family":"Khawaja","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University, Changsha 401302, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1016\/j.ins.2018.09.035","article-title":"Synchronization-based clustering on evolving data stream","volume":"501","author":"Shao","year":"2019","journal-title":"Inf. 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