{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T10:41:02Z","timestamp":1768473662394,"version":"3.49.0"},"reference-count":71,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,4,19]],"date-time":"2022-04-19T00:00:00Z","timestamp":1650326400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Exploring a community is an important aspect of social network analysis because it can be seen as a crucial way to decompose specific graphs into smaller graphs based on interactions between users. The process of discovering common features between groups of users, entitled \u201ccommunity detection\u201d, is a fundamental feature for social network analysis, wherein the vertices represent the users and the edges their relationships. Our study focuses on identifying such phenomena on the Twitter graph of posts and on determining communities, which contain users with similar features. This paper presents the evaluation of six established community-discovery algorithms, namely Breadth-First Search, CNM, Louvain, MaxToMin, Newman\u2013Girvan and Propinquity Dynamics, in terms of four widely used graphs and a collection of data fetched from Twitter about man-made and physical data. Furthermore, the size of each community, expressed as a percentage of the total number of vertices, is identified for the six particular algorithms, and corresponding results are extracted. In terms of user-based evaluation, we indicated to some students the communities that were extracted by every algorithm, with a corresponding user and their tweets in the grouping and considered three different alternatives for the extracted communities: \u201cdense community\u201d, \u201csparse community\u201d and \u201cin-between\u201d. Our findings suggest that the community-detection algorithms can assist in identifying dense group of users.<\/jats:p>","DOI":"10.3390\/info13050209","type":"journal-article","created":{"date-parts":[[2022,4,19]],"date-time":"2022-04-19T22:07:26Z","timestamp":1650406046000},"page":"209","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Evaluating Methods for Efficient Community Detection in Social Networks"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9964-4134","authenticated-orcid":false,"given":"Andreas","family":"Kanavos","sequence":"first","affiliation":[{"name":"Department of Digital Media and Communication, Ionian University, 28100 Kefalonia, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4039-7714","authenticated-orcid":false,"given":"Yorghos","family":"Voutos","sequence":"additional","affiliation":[{"name":"Department of Informatics, Ionian University, 49100 Corfu, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5443-1613","authenticated-orcid":false,"given":"Foteini","family":"Grivokostopoulou","sequence":"additional","affiliation":[{"name":"Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6916-3129","authenticated-orcid":false,"given":"Phivos","family":"Mylonas","sequence":"additional","affiliation":[{"name":"Department of Informatics, Ionian University, 49100 Corfu, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1007\/s10618-011-0224-z","article-title":"Community Detection in Social Media","volume":"24","author":"Papadopoulos","year":"2012","journal-title":"Data Min. 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