{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T16:14:18Z","timestamp":1781108058441,"version":"3.54.1"},"reference-count":13,"publisher":"IGI Global Scientific Publishing","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,1,1]]},"abstract":"<p>Social network analysis has gained much importance these days. Social network analysis is the process of recording various patterns of interactions between a set of social entities. An important phenomenon that draws the attention of analysis is the emergence of communities in these networks. The understanding and detection of communities in these networks is a challenging research problem. However, approaches to detect communities have largely focused on identifying communities in static social networks. But real-world social networks are not always static. In fact, many social networks in reality (such as Facebook, Bebo and Twitter) are dynamic networks that frequently change over time. In this paper, a framework is proposed for community detection in dynamic social networks, which explores self-organizing maps (SOM) for cluster selection and modularity measure for community strength identification. Experimental results on synthetic network datasets show the effectiveness of the proposed approach.<\/p>","DOI":"10.4018\/ijrsda.2018010103","type":"journal-article","created":{"date-parts":[[2017,10,17]],"date-time":"2017-10-17T13:56:37Z","timestamp":1508248597000},"page":"34-43","source":"Crossref","is-referenced-by-count":2,"title":["Detecting Communities in Dynamic Social Networks using Modularity Ensembles SOM"],"prefix":"10.4018","volume":"5","author":[{"given":"Raju","family":"Enugala","sequence":"first","affiliation":[{"name":"SR Engineering College, Department of Computer Science and Engineering, Warangal, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lakshmi","family":"Rajamani","sequence":"additional","affiliation":[{"name":"Osmania University, Hyderabad, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sravanthi","family":"Kurapati","sequence":"additional","affiliation":[{"name":"Kakatiya University, Warangal, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad Ali","family":"Kadampur","sequence":"additional","affiliation":[{"name":"Saudi Electronic University, Department of Computer Science & Information Technology, Riyadh, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Y. Rama","family":"Devi","sequence":"additional","affiliation":[{"name":"Chaitanya Bharathi Institute of Technology, Hyderabad, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"IJRSDA.2018010103-0","doi-asserted-by":"publisher","DOI":"10.1145\/1281192.1281290"},{"key":"IJRSDA.2018010103-1","doi-asserted-by":"publisher","DOI":"10.1016\/j.physrep.2005.10.009"},{"key":"IJRSDA.2018010103-2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.122653799"},{"key":"IJRSDA.2018010103-3","article-title":"On the bursty evolution of blogspace.","author":"R.Kumar","year":"2003","journal-title":"Proceedings of the 12th WWW Conference"},{"key":"IJRSDA.2018010103-4","first-page":"80","article-title":"Community detection algorithms: A comparative analysis.","author":"A.Lancichinetti","year":"2009","journal-title":"Physical Review E: Statistical, Nonlinear, and Soft Matter Physics"},{"key":"IJRSDA.2018010103-5","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.69.026113"},{"key":"IJRSDA.2018010103-6","doi-asserted-by":"publisher","DOI":"10.1109\/HIS.2009.268"},{"key":"IJRSDA.2018010103-7","doi-asserted-by":"publisher","DOI":"10.1038\/nature05670"},{"key":"IJRSDA.2018010103-8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-01284-6_2"},{"key":"IJRSDA.2018010103-9","doi-asserted-by":"crossref","unstructured":"Raju, E., Lakshmi, R., Kadampur, A., & Sravanthi, K. (2016). Identifying Natural Communities in Social Networks Using Modularity Coupled with Self-Organizing Maps. In Proceedings of theInternational Conference on Computational Intelligence in Data Mining(ICCIDM) (Vol. 1, pp. 367 \u2013 376). Springer. DOI 10.1007\/978-81-322-2734-2_37.","DOI":"10.1007\/978-81-322-2734-2_37"},{"key":"IJRSDA.2018010103-10","doi-asserted-by":"publisher","DOI":"10.1145\/1281192.1281266"},{"key":"IJRSDA.2018010103-11","first-page":"1","article-title":"Web Mining Techniques for Online Social Networks Analysis.","author":"I. 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