Modularity of Directed Networks: Cycle Decomposition Approach

  • The problem of decomposing networks into modules (or clusters) has gained much attention in recent years, as it can account for a coarsegrained description of complex systems, often revealing functional subunits of these systems. A variety of module detection algorithms have been proposed, mostly oriented towards finding hard partitionings of undirected networks. Despite the increasing number of fuzzy clustering methods for directed networks, many of these approaches tend to neglect important directional information. In this paper, we present a novel random walk based approach for finding fuzzy partitions of directed, weighted networks, where edge directions play a crucial role in defining how well nodes in a module are interconnected. We will show that cycle decomposition of a random walk process connects the notion of network modules and information transport in a network, leading to a new, symmetric measure of node communication. Finally, we will use this measure to introduce a communication graph, for which we will show that although being undirected it inherits all necessary information about modular structures from the original network.

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Metadaten
Author:Natasa Djurdjevac Conrad, Ralf Banisch, Christof Schütte
Document Type:Article
Parent Title (English):Journal of Computational Dynamics 2 (2015) pp. 1-24
Year of first publication:2014
Preprint:urn:nbn:de:0297-zib-51166
DOI:http://dx.doi.org/10.3934/jcd.2015.2.1

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