Size reduction of complex networks preserving modularity

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dc.creatorArenas, A.
dc.creatorDuch, J.
dc.creatorFernandez, A.
dc.creatorGomez, S.
dc.date2007-02-02
dc.date.accessioned2026-07-07T08:20:50Z
dc.date.available2026-07-07T08:20:50Z
dc.descriptionThe ubiquity of modular structure in real-world complex networks is being the focus of attention in many trials to understand the interplay between network topology and functionality. The best approaches to the identification of modular structure are based on the optimization of a quality function known as modularity. However this optimization is a hard task provided that the computational complexity of the problem is in the NP-hard class. Here we propose an exact method for reducing the size of weighted (directed and undirected) complex networks while maintaining invariant its modularity. This size reduction allows the heuristic algorithms that optimize modularity for a better exploration of the modularity landscape. We compare the modularity obtained in several real complex-networks by using the Extremal Optimization algorithm, before and after the size reduction, showing the improvement obtained. We speculate that the proposed analytical size reduction could be extended to an exact coarse graining of the network in the scope of real-space renormalization.
dc.description14 pages, 2 figures
dc.identifierhttps://arxiv.org/abs/physics/0702015
dc.identifierhttp://arxiv.org/abs/physics/0702015
dc.identifierNew Journal of Physics 9 (2007) 176
dc.identifierdoi:10.1088/1367-2630/9/6/176
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/135183
dc.subjectComputational Physics
dc.subjectOther Condensed Matter
dc.subjectDiscrete Mathematics
dc.subjectData Analysis, Statistics and Probability
dc.subjectQuantitative Methods
dc.titleSize reduction of complex networks preserving modularity
dc.typetext

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