A very few strategies are specially designed for dealing with directed networks.We propose a community detection in directed networks algorithm, ConClus.ConClus outperformed the best algorithms found in the literature in dense networks. 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.
['Camila Pereira Santos', 'Desiree Maldonado Carvalho', 'Mariá Cristina Vasconcelos Nascimento']
A consensus graph clustering algorithm for directed networks