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Differently from theoretical scale-free networks, most of real networkspresent multi-scale behavior with nodes structured in different types offunctional groups and communities. While the majority of approaches forclassification of nodes in a complex network has relied on local measurementsof the topology/connectivity around each node, valuable information about nodefunctionality can be obtained by Concentric (or Hierarchical) Measurements. Inthis paper we explore the possibility of using a set of Concentric Measurementsand agglomerative clustering methods in order to obtain a set of functionalgroups of nodes. Concentric clustering coefficient and convergence ratio arechosen as segregation parameters for the analysis of a institutionalcollaboration network including various known communities (departments of theUniversity of S\~ao Paulo). A dendogram is obtained and the results areanalyzed and discussed. Among the interesting obtained findings, we emphasizethe scale-free nature of the obtained network, as well as the identification ofdifferent patterns of authorship emerging from different areas (e.g. human andexact sciences). Another interesting result concerns the relatively uniformdistribution of hubs along the concentric levels, contrariwise to thenon-uniform pattern found in theoretical scale free networks such as the BAmodel.

Concentric Characterization and Classification of Complex Network Nodes: Theory and Application to Institutional Collaboration