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Mathematical Problems in Engineering
Volume 2014 (2014), Article ID 502809, 15 pages
Research Article

Performance Evaluation of Modularity Based Community Detection Algorithms in Large Scale Networks

1Department of Computer Science, Federal University of São João del Rei (UFSJ), 36301-360 São João del Rei, MG, Brazil
2COPPE, Federal University of Rio de Janeiro (UFRJ), P.O. Box 68506, 21941-972 Rio de Janeiro, RJ, Brazil

Received 28 August 2014; Accepted 27 November 2014; Published 28 December 2014

Academic Editor: Mohamed A. Seddeek

Copyright © 2014 Vinícius da Fonseca Vieira 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.


Community structure detection is one of the major research areas of network science and it is particularly useful for large real networks applications. This work presents a deep study of the most discussed algorithms for community detection based on modularity measure: Newman’s spectral method using a fine-tuning stage and the method of Clauset, Newman, and Moore (CNM) with its variants. The computational complexity of the algorithms is analysed for the development of a high performance code to accelerate the execution of these algorithms without compromising the quality of the results, according to the modularity measure. The implemented code allows the generation of partitions with modularity values consistent with the literature and it overcomes 1 million nodes with Newman’s spectral method. The code was applied to a wide range of real networks and the performances of the algorithms are evaluated.