The analysis of complex networks is devoted to the statistical characterization of the topology of graphs at different scales of organization in order to understand their functionality. While the modular structure of networks has become an essential element to better apprehend their complexity, the efforts to characterize the mesoscale of networks have focused on the identification of the modules rather than describing the mesoscale in an informative manner. Here we propose a framework to characterize the position every node takes within the modular configuration of complex networks and to evaluate their function accordingly. For illustration, we apply this framework to a set of synthetic networks, empirical neural networks, and to the transcriptional regulatory network of the Mycobacterium tuberculosis. We find that the architecture of both neuronal and transcriptional networks are optimized for the processing of multisensory information with the coexistence of well-defined modules of specialized components and the presence of hubs conveying information from and to the distinct functional domains.
Bibliographical noteDate of Acceptance: 06/11/2014
We are thankful to Prof Alex Arenas, Dr Sergio Gómez, Veronika Stolbova and Dominik Traxl for their helpful comments. We also thank Joaquín Sanz Remón for kindly providing the data of the Tuberculosis RT network and for his valuable comments. This work has been supported by (JK) the German Federal Ministry of Education and Research (Bernstein Center II, grant no. 01GQ1001A), (FK) the Engineering and Physical Sciences Research Council, and (GZL) the European Union Seventh Framework Programme FP7/2007-2013 under grant agreement number PIEF- GA-2012-331800.