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A threshold value for each boundary node is used as the minimum influence by the neighbours of a node in order to determine its belongingness to any community. The effectiveness of the EnDNTM algorithm has been demonstrated by testing with five synthetic benchmark datasets and fifteen real-world datasets. The performance of the EnDNTM algorithm was compared with seven overlapping community detection algorithms. The F1-score, normalized mutual information ONMI and extended modularity Qo\u2062v metrics were used to measure the quality of the detected communities. EnDNTM outperforms comparable algorithms on 4 out of 5 synthetic benchmarks datasets, 11 out of 15 real world datasets and gives comparable results with the remaining datasets. Experiments on various synthetic and real world datasets reveal that for a majority of datasets, the proposed ensemble-based distributed neighbourhood threshold method is able to select the best disjoint clusters produced by a disjoint method from a collection of methods for detecting overlapping communities.<\/jats:p>","DOI":"10.3233\/idt-200059","type":"journal-article","created":{"date-parts":[[2021,5,25]],"date-time":"2021-05-25T12:40:53Z","timestamp":1621946453000},"page":"251-267","source":"Crossref","is-referenced-by-count":4,"title":["Detecting overlapping communities using ensemble-based distributed neighbourhood threshold method in social networks"],"prefix":"10.1177","volume":"15","author":[{"given":"Rajesh","family":"Jaiswal","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sheela","family":"Ramanna","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"12","key":"10.3233\/IDT-200059_ref1","doi-asserted-by":"crossref","first-page":"7821","DOI":"10.1073\/pnas.122653799","article-title":"Community structure in social and biological networks","volume":"99","author":"Girvan","year":"2002","journal-title":"Proceedings of the National Academy of Sciences"},{"issue":"4","key":"10.3233\/IDT-200059_ref2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2501654.2501657","article-title":"Overlapping community detection in networks: The state-of-the-art and comparative study","volume":"45","author":"Xie","year":"2013","journal-title":"Acm Computing Surveys (CSUR)"},{"issue":"3-5","key":"10.3233\/IDT-200059_ref3","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.physrep.2009.11.002","article-title":"Community detection in graphs","volume":"486","author":"Fortunato","year":"2010","journal-title":"Physics Reports"},{"issue":"4","key":"10.3233\/IDT-200059_ref4","doi-asserted-by":"crossref","first-page":"1118","DOI":"10.1073\/pnas.0706851105","article-title":"Maps of random walks on complex networks reveal community structure","volume":"105","author":"Rosvall","year":"2008","journal-title":"Proceedings of the National Academy of Sciences"},{"key":"10.3233\/IDT-200059_ref5","doi-asserted-by":"crossref","unstructured":"Blondel VD, Guillaume JL, Lambiotte R, Lefebvre E. 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