Distributed Centrality Analysis of Social Network Data Using MapReduce
AbstractAnalyzing 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
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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.
Kumar Behera R, Kumar Rath S, Misra S, Damaševičius R, Maskeliūnas R. Distributed Centrality Analysis of Social Network Data Using MapReduce. Algorithms. 2019; 12(8):161.Chicago/Turabian Style
Kumar Behera, Ranjan; Kumar Rath, Santanu; Misra, Sanjay; Damaševičius, Robertas; Maskeliūnas, Rytis. 2019. "Distributed Centrality Analysis of Social Network Data Using MapReduce." Algorithms 12, no. 8: 161.
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