{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T22:55:00Z","timestamp":1791413700463,"version":"4.3.3"},"reference-count":39,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2016,8,22]],"date-time":"2016-08-22T00:00:00Z","timestamp":1471824000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,9,1]]},"abstract":"<jats:title>Summary<\/jats:title>\n                  <jats:p>Statistical node clustering in discrete time dynamic networks is an emerging field that raises many challenges. Here, we explore statistical properties and frequentist inference in a model that combines a stochastic block model for its static part with independent Markov chains for the evolution of the nodes groups through time. We model binary data as well as weighted dynamic random graphs (with discrete or continuous edges values). Our approach, motivated by the importance of controlling for label switching issues across the different time steps, focuses on detecting groups characterized by a stable within-group connectivity behaviour. We study identifiability of the model parameters and propose an inference procedure based on a variational expectation\u2013maximization algorithm as well as a model selection criterion to select the number of groups. We carefully discuss our initialization strategy which plays an important role in the method and we compare our procedure with existing procedures on synthetic data sets. We also illustrate our approach on dynamic contact networks: one of encounters between high school students and two others on animal interactions. An implementation of the method is available as an R package called dynsbm.<\/jats:p>","DOI":"10.1111\/rssb.12200","type":"journal-article","created":{"date-parts":[[2016,8,23]],"date-time":"2016-08-23T00:26:45Z","timestamp":1471912005000},"page":"1119-1141","source":"Crossref","is-referenced-by-count":268,"title":["Statistical Clustering of Temporal Networks Through a Dynamic Stochastic Block Model"],"prefix":"10.1093","volume":"79","author":[{"given":"Catherine","family":"Matias","sequence":"first","affiliation":[{"name":"Centre National de la Recherche Scientifique \u00a0 , Paris , France"},{"name":"Universit\u00e9 Pierre et Marie Curie and Laboratoire de Probabilit\u00e9s et Mod\u00e8les Al\u00e9atoires \u00a0 , Paris , France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vincent","family":"Miele","sequence":"additional","affiliation":[{"name":"Universit\u00e9 de Lyon, Universit\u00e9 Lyon 1, Centre National de la Recherche Scientifique and Laboratoire de Biom\u00e9trie et Biologie \u00c9volutive , Villeurbanne , France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2016,8,22]]},"reference":[{"key":"2023021709330105300_rssb12200-cit-0001","first-page":"1981","article-title":"Mixed-membership stochastic block-models","volume":"9","author":"Airoldi","year":"2008","journal-title":"J. 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