Modular structure is ubiquitous in real-world complex networks. The detection of this type of organization into modules gives insights in the relationship between topological structure and functionality. The best approaches to the identification of modular structure are based on the optimization of a quality function known as modularity, which is a relative quality measure for a partition of a network into modules or “communities”. Recently some authors pointed out that the optimization of modularity has a resolution limit beyond which no modular structure can be detected even though these modules might have own entity. Here we reanalyze this problem and propose a method that allows for multiple resolution screening of the modular structure, releasing the optimization of modularity from resolution problems, and accessing to new scales of description of complex networks while preserving the topological properties. The method has been applied to synthetic and real networks obtainin...