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Mathematical Problems in Engineering
Volume 2014, Article ID 580647, 9 pages
http://dx.doi.org/10.1155/2014/580647
Research Article

A Modularity Degree Based Heuristic Community Detection Algorithm

Software College, Northeastern University, Shenyang 110819, China

Received 11 October 2013; Revised 6 January 2014; Accepted 7 January 2014; Published 25 February 2014

Academic Editor: Yuncai Wang

Copyright © 2014 Dongming Chen 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

A community in a complex network can be seen as a subgroup of nodes that are densely connected. Discovery of community structures is a basic problem of research and can be used in various areas, such as biology, computer science, and sociology. Existing community detection methods usually try to expand or collapse the nodes partitions in order to optimize a given quality function. These optimization function based methods share the same drawback of inefficiency. Here we propose a heuristic algorithm (MDBH algorithm) based on network structure which employs modularity degree as a measure function. Experiments on both synthetic benchmarks and real-world networks show that our algorithm gives competitive accuracy with previous modularity optimization methods, even though it has less computational complexity. Furthermore, due to the use of modularity degree, our algorithm naturally improves the resolution limit in community detection.