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Modular structure is ubiquitous in real-world complex networks, and itsdetection is important because it gives insights in the structure-functionalityModular structure is ubiquitous in real-world complex networks, and itsdetection is important because it gives insights in the structure-functionalityrelationship. The standard approach is based on the optimization of a qualityfunction, modularity, which is a relative quality measure for a partition of anetwork into modules. Recently some authors [1,2] have pointed out that theoptimization of modularity has a fundamental drawback: the existence of aresolution limit beyond which no modular structure can be detected even thoughthese modules might have own entity. The reason is that several topologicaldescriptions of the network coexist at different scales, which is, in general,a fingerprint of complex systems. Here we propose a method that allows formultiple resolution screening of the modular structure. The method has beenvalidated using synthetic networks, discovering the predefined structures atall scales. Its application to two real social networks allows to find theexact splits reported in the literature, as well as the substructure beyond theactual split.Comment: 23 pages, 5 figure

Analysis of the structure of complex networks at different resolution levels