Community detection by consensus genetic-based algorithm for directed networks

Community detection by consensus genetic-based algorithm for directed networks

Author Mathias, Stefano B. B. R. P. Autor UNIFESP Google Scholar
Rosset, Valerio Autor UNIFESP Google Scholar
Nascimento, Maria C. V. Autor UNIFESP Google Scholar
Abstract Finding communities in networks is a commonly used form of network analysis. There is a myriad of community detection algorithms in the literature to perform this task. In spite of that, the number of community detection algorithms in directed networks is much lower than in undirected networks. However, evaluation measures to estimate the quality of communities in undirected networks nowadays have its adaptation to directed networks as, for example, the well-known modularity measure. This paper introduces a genetic-based consensus clustering to detect communities in directed networks with the directed modularity as the fitness function. Consensus strategies involve combining computational models to improve the quality of solutions generated by a single model. The reason behind the development of a consensus strategy relies on the fact that recent studies indicate that the modularity may fail in detecting expected clusterings. Computational experiments with artificial LFR networks show that the proposed method was very competitive in comparison to existing strategies in the literature. (C) 2016 The Authors. Published by Elsevier B.V.
Keywords Genetic Algorithms
Consensus Clustering
Directed NetworksComplex Networks
Memetic Algorithm
Language English
Date 2016
Published in Procedia Computer Science, Amsterdam, v. 96, p. 90-99, 2016.
ISSN 1877-0509 (Sherpa/Romeo, impact factor)
Publisher Wiley-Blackwell
Extent 90-99
Access rights Open access Open Access
Type Conference paper
Web of Science ID WOS:000383252400010

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