Large Scale Community Detection Using a Small World Model

dc.authorid BEHERA, RANJAN KUMAR/0000-0001-9267-3621
dc.authorid Damaševičius, Robertas/0000-0001-9990-1084
dc.authorid Maskeliunas, Rytis/0000-0002-2809-2213
dc.authorid Misra, Sanjay/0000-0002-3556-9331
dc.authorid Rath, Santanu/0000-0001-5641-8199
dc.authorscopusid 55185224200
dc.authorscopusid 55428272300
dc.authorscopusid 56962766700
dc.authorscopusid 6603451290
dc.authorscopusid 27467587600
dc.authorwosid BEHERA, RANJAN KUMAR/I-2680-2017
dc.authorwosid Damaševičius, Robertas/E-1387-2017
dc.authorwosid Maskeliunas, Rytis/J-7173-2017
dc.authorwosid Misra, Sanjay/K-2203-2014
dc.authorwosid Rath, Santanu/O-6685-2017
dc.contributor.author Behera, Ranjan Kumar
dc.contributor.author Rath, Santanu Kumar
dc.contributor.author Misra, Sanjay
dc.contributor.author Damasevicius, Robertas
dc.contributor.author Maskeliunas, Rytis
dc.contributor.other Computer Engineering
dc.date.accessioned 2024-07-05T15:29:36Z
dc.date.available 2024-07-05T15:29:36Z
dc.date.issued 2017
dc.department Atılım University en_US
dc.department-temp [Behera, Ranjan Kumar; Rath, Santanu Kumar] Natl Inst Technol, Dept Comp Sci & Engn, Rourkela 769008, India; [Misra, Sanjay] Atilim Univ, Dept Comp Engn, TR-06836 Ankara, Turkey; [Misra, Sanjay] Covenant Univ, Dept Elect & Informat Engn, Ota 1023, Nigeria; [Damasevicius, Robertas; Maskeliunas, Rytis] Kaunas Univ Technol, Dept Multimedia Engn, LT-51368 Kaunas, Lithuania; [Behera, Ranjan Kumar] NIT Rourkela, Dept Comp Sci & Engn, Rourkela 769008, Odisha, India en_US
dc.description BEHERA, RANJAN KUMAR/0000-0001-9267-3621; Damaševičius, Robertas/0000-0001-9990-1084; Maskeliunas, Rytis/0000-0002-2809-2213; Misra, Sanjay/0000-0002-3556-9331; Rath, Santanu/0000-0001-5641-8199 en_US
dc.description.abstract In a social network, small or large communities within the network play a major role in deciding the functionalities of the network. Despite of diverse definitions, communities in the network may be defined as the group of nodes that are more densely connected as compared to nodes outside the group. Revealing such hidden communities is one of the challenging research problems. A real world social network follows small world phenomena, which indicates that any two social entities can be reachable in a small number of steps. In this paper, nodes are mapped into communities based on the random walk in the network. However, uncovering communities in large-scale networks is a challenging task due to its unprecedented growth in the size of social networks. A good number of community detection algorithms based on random walk exist in literature. In addition, when large-scale social networks are being considered, these algorithms are observed to take considerably longer time. In this work, with an objective to improve the efficiency of algorithms, parallel programming framework like Map-Reduce has been considered for uncovering the hidden communities in social network. The proposed approach has been compared with some standard existing community detection algorithms for both synthetic and real-world datasets in order to examine its performance, and it is observed that the proposed algorithm is more efficient than the existing ones. en_US
dc.description.sponsorship Covenant University Centre for Research and Innovation Development, Ota, Nigeria; Faculty of Informatics, Kaunas University of Technology, Kaunas, Lithuania en_US
dc.description.sponsorship Thanks to the authorities of the NIT, Rourkela for availing the platform for doing this research study. Support also came from Covenant University Centre for Research and Innovation Development, Ota, Nigeria; and Research Cluster Fund of Faculty of Informatics, Kaunas University of Technology, Kaunas, Lithuania. en_US
dc.identifier.citationcount 32
dc.identifier.doi 10.3390/app7111173
dc.identifier.issn 2076-3417
dc.identifier.issue 11 en_US
dc.identifier.scopus 2-s2.0-85034256334
dc.identifier.uri https://doi.org/10.3390/app7111173
dc.identifier.uri https://hdl.handle.net/20.500.14411/2941
dc.identifier.volume 7 en_US
dc.identifier.wos WOS:000416794600072
dc.identifier.wosquality Q2
dc.institutionauthor Mısra, Sanjay
dc.language.iso en en_US
dc.publisher Mdpi en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.scopus.citedbyCount 35
dc.subject small world network en_US
dc.subject six degrees of separation en_US
dc.subject map reduce en_US
dc.subject community detection en_US
dc.subject modularity en_US
dc.subject normalize mutual information en_US
dc.title Large Scale Community Detection Using a Small World Model en_US
dc.type Article en_US
dc.wos.citedbyCount 31
dspace.entity.type Publication
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