A consensus graph clustering algorithm for directed networks |
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| Affiliation: | 1. Institute of New Imaging Technologies, Department of Computer Languages and Systems, Universitat Jaume I, Castelló de la Plana, Spain;2. División Multidisciplinaria de Ciudad Universitaria, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez, Chihuahua, Mexico;3. School of Engineering, Universidad Autónoma del Estado de México, Toluca, Mexico;1. Department of Computer Sciences and Automatic Control, UNED, Juan del Rosal 16, 28040 Madrid, Spain;2. National Fusion Laboratory by Magnetic Confinement, CIEMAT, Complutense 40, 28040 Madrid, Spain;1. Faculty of Information Science and Technology, Multimedia University, Jalan Ayer Keroh Lama, 75450 Melaka, Malaysia;2. Faculty of Engineering and Technology, Multimedia University, Jalan Ayer Keroh Lama, 75450 Melaka, Malaysia;1. Graduate Program in Applied Computing (PPGCA);2. Graduate Program in Electrical and Computer Engineering (CPGEI), Federal University of Technology - Parana (UTFPR). Av. Sete de Setembro, 3165. CEP 80230-901, Curitiba, Brazil;3. Institut National de Recherche en Informatique et en Automatique (INRIA) Saclay - Ile de France. 4, rue Jacques Monod, 91893 Orsay Cedex, France;1. Research Center of Intelligent Signal Processing (RCISP), Tehran, Iran;2. Department of Biomedical Engineering, Amirkabir University of Technology, 424 Hafez Ave, Tehran 15875-4413, Iran |
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| Abstract: | Finding groups of highly related vertices in undirected graphs has been widely investigated. Nevertheless, a very few strategies are specially designed for dealing with directed networks. In particular, strategies based on the maximization of the modularity adjusted to overcome the resolution limit for directed networks have not been developed. The analysis of the characteristics of the clusters produced by these approaches is highly important since among the most used strategies for detecting communities in directed networks are the modularity maximization-based algorithms for undirected graphs. Towards these remarks, in this paper we propose a consensus-based strategy, named ConClus, for providing partitions for directed networks guided by the adjusted modularity measure. In the computational experiments, we compared ConClus with benchmark strategies, including Infomap and OSLOM, by using hundreds of LFR networks. ConClus outperformed Infomap and was competitive with OSLOM even for graphs with high mixture index and small-sized clusters, to which modularity-based algorithms have limitations. ConClus outperformed all algorithms when considering the networks with the highest average and maximum in-degrees among the networks used in the experiments. |
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