  Modular structure is ubiquitous in real-world complex networks, and its
detection is important because it gives insights in the structure-functionality
Modular structure is ubiquitous in real-world complex networks, and its
detection is important because it gives insights in the structure-functionality
relationship. The standard approach is based on the optimization of a quality
function, modularity, which is a relative quality measure for a partition of a
network into modules. Recently some authors [1,2] have pointed out that the
optimization of modularity has a fundamental drawback: the existence of a
resolution limit beyond which no modular structure can be detected even though
these modules might have own entity. The reason is that several topological
descriptions of the network coexist at different scales, which is, in general,
a fingerprint of complex systems. Here we propose a method that allows for
multiple resolution screening of the modular structure. The method has been
validated using synthetic networks, discovering the predefined structures at
all scales. Its application to two real social networks allows to find the
exact splits reported in the literature, as well as the substructure beyond the
actual split.
