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Appl. Sci. 2017, 7(11), 1173;

Large Scale Community Detection Using a Small World Model

Department of Computer Science and Engineering, National Institute of Technology, Rourkela 769008, India
Department of Computer Engineering, Atilim University, Incek, Ankara 06836, Turkey
Department of Electrical and Information Engineering, Covenant University, Ota 1023, Nigeria
Department of Multimedia Engineering, Kaunas University of Technology, Kaunas 51368, Lithuania
Authors to whom correspondence should be addressed.
Current address: Department of Computer Science and Engineering, NIT Rourkela, Rourkela 769008, Odisha, India.
Received: 27 September 2017 / Revised: 1 November 2017 / Accepted: 2 November 2017 / Published: 15 November 2017
(This article belongs to the Special Issue Socio-Cognitive and Affective Computing)
PDF [870 KB, uploaded 16 November 2017]


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. View Full-Text
Keywords: small world network; six degrees of separation; map reduce; community detection; modularity; normalize mutual information small world network; six degrees of separation; map reduce; community detection; modularity; normalize mutual information

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Behera, R.K.; Rath, S.K.; Misra, S.; Damaševičius, R.; Maskeliūnas, R. Large Scale Community Detection Using a Small World Model. Appl. Sci. 2017, 7, 1173.

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