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Despite the extensive global researches conducted on these, discovery has not been confirmed yet and researchers try to find methods and improve estimated techniques by using Data Mining tools, Graph Mining tools and artificial intelligence techniques. This paper proposes a novel two-phase approach based on global and local information to detect communities in social network. It explores the global information in the first phase and then exploits the local information in the second phase to discover communities more accurately. It also proposes a novel algorithm which exploits the local information and mines deeply for the second phase. Experimental results show that the proposed method has better performance and achieves more accurate results compared with the previous ones.<\/jats:p>","DOI":"10.1177\/0165551516657717","type":"journal-article","created":{"date-parts":[[2016,7,4]],"date-time":"2016-07-04T21:48:36Z","timestamp":1467668916000},"page":"615-634","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":19,"title":["Community detection in dynamic social networks: A local evolutionary approach"],"prefix":"10.1177","volume":"43","author":[{"given":"Mohammad Ebrahim","family":"Samie","sequence":"first","affiliation":[{"name":"Shiraz University, Iran"}]},{"given":"Ali","family":"Hamzeh","sequence":"additional","affiliation":[{"name":"Shiraz University, 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