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Open AccessArticle

Algorithm for Detecting Communities in Complex Networks Based on Hadoop

School of Information, Central University of Finance and Economics, Beijing 100081, China
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Symmetry 2019, 11(11), 1382; https://doi.org/10.3390/sym11111382
Received: 9 October 2019 / Revised: 31 October 2019 / Accepted: 6 November 2019 / Published: 7 November 2019
(This article belongs to the Special Issue Recent Advances in Social Data and Artificial Intelligence 2019)
With the explosive growth of the scale of complex networks, the existing community detection algorithms are unable to meet the needs of rapid analysis of the community structure in complex networks. A new algorithm for detecting communities in complex networks based on the Hadoop platform (called Community Detection on Hadoop (CDOH)) is proposed in this paper. Based on the basic idea of modularity increment, our algorithm implements parallel merging and accomplishes a fast and accurate detection of the community structure in complex networks. Our extensive experimental results on three real datasets of complex networks demonstrate that the CDOH algorithm can improve the efficiency of the current memory-based community detection algorithms significantly without affecting the accuracy of the community detection.
Keywords: community detection; complex networks; Hadoop; modularity increment community detection; complex networks; Hadoop; modularity increment
MDPI and ACS Style

Hai , M.; Li, H.; Ma, Z.; Gao, X. Algorithm for Detecting Communities in Complex Networks Based on Hadoop. Symmetry 2019, 11, 1382.

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