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Open AccessFeature PaperArticle

Distributed Centrality Analysis of Social Network Data Using MapReduce

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
Faculty of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland
Department of Multimedia Engineering, Kaunas University of Technology, 51368 Kaunas, Lithuania
Author to whom correspondence should be addressed.
Algorithms 2019, 12(8), 161;
Received: 1 July 2019 / Revised: 4 August 2019 / Accepted: 7 August 2019 / Published: 9 August 2019
(This article belongs to the Special Issue Algorithms for Pattern Recognition)
PDF [1231 KB, uploaded 9 August 2019]


Analyzing the structure of a social network helps in gaining insights into interactions and relationships among users while revealing the patterns of their online behavior. Network centrality is a metric of importance of a network node in a network, which allows revealing the structural patterns and morphology of networks. We propose a distributed computing approach for the calculation of network centrality value for each user using the MapReduce approach in the Hadoop platform, which allows faster and more efficient computation as compared to the conventional implementation. A distributed approach is scalable and helps in efficient computations of large-scale datasets, such as social network data. The proposed approach improves the calculation performance of degree centrality by 39.8%, closeness centrality by 40.7% and eigenvalue centrality by 41.1% using a Twitter dataset. View Full-Text
Keywords: distributed computing; social network analysis; network centrality; network pattern recognition; MapReduce distributed computing; social network analysis; network centrality; network pattern recognition; MapReduce

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Kumar Behera, R.; Kumar Rath, S.; Misra, S.; Damaševičius, R.; Maskeliūnas, R. Distributed Centrality Analysis of Social Network Data Using MapReduce. Algorithms 2019, 12, 161.

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