The analysis of complex networks is devoted to the statistical characterization of/nthe topology of graphs at different scales of organization in order to understand/ntheir functionality. While the modular structure of networks has become an/nessential element to better apprehend their complexity, the efforts to characterize/nthe mesoscale of networks have focused on the identification of the modules/nrather than describing the mesoscale in an informative manner. Here we propose/na framework to characterize the position every node takes within the modular/nconfiguration of complex networks and to evaluate their function accordingly./nFor illustration, we apply this framework to a set of synthetic networks,/nempirical neural networks, and to the transcriptional regulatory network of the/nMycobacterium tuberculosis.Wefind that the architecture of both neuronal and/ntranscriptional networks are optimized for the processing of multisensory information with the coexistence of well-de/nfined modules of specialized components and the presence of hubs conveying information from and to the/ndistinct functional domains

We are thankful to Prof Alex Arenas, Dr Sergio Gómez, Veronika Stolbova and Dominik Traxl/nfor their helpful comments. We also thank Joaquín Sanz Remón for kindly providing the data of/nthe Tuberculosis RT network and for his valuable comments. This work has been supported by/n(JK) the German Federal Ministry of Education and Research (Bernstein Center II, grant no./n01GQ1001A), (FK) the Engineering and Physical Sciences Research Council, and (GZL) the/nEuropean Union Seventh Framework Programme FP7/2007-2013 under grant agreement/nnumber PIEF- GA-2012-331800.