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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.