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Mathematical Problems in Engineering
Volume 2015, Article ID 934301, 13 pages
http://dx.doi.org/10.1155/2015/934301
Research Article

A Parallel Community Structure Mining Method in Big Social Networks

1College of Computer, National University of Defense Technology, Changsha, Hunan 410073, China
2Department of Computer Science, University of Illinois at Chicago, Chicago, IL 60607, USA

Received 5 July 2014; Accepted 2 August 2014

Academic Editor: Haipeng Peng

Copyright © 2015 Songchang Jin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Community structure plays a key role in analyzing network features and helping people to dig out valuable hidden information. However, how to discover the hidden community structures is one of the biggest challenges in social network analysis, especially when the network size swells to a high level. Infomap is a top-class algorithm in nonoverlapping community structure detection. However, it is designed for single processor. When tackling large networks, its limited scalability makes it less effective in fully utilizing server resources. In this paper, based on infomap, we develop a scalable parallel nonoverlapping community detection method, Pinfomr (parallel Infomap with MapReduce), which utilizes the MapReduce framework to solve the two problems. Experiments on artificial networks and real datasets show that our parallel method has satisfying performance and scalability.