  The analysis of the modular structure of networks is a major challenge in
complex networks theory. The validity of the modular structure obtained is
essential to confront the problem of the topology-functionality relationship.
Recently, several authors have worked on the limit of resolution that different
community detection algorithms have, making impossible the detection of natural
modules when very different topological scales coexist in the network. Existing
multiresolution methods are not the panacea for solving the problem in extreme
situations, and also fail. Here, we present a new hierarchical multiresolution
scheme that works even when the network decomposition is very close to the
resolution limit. The idea is to split the multiresolution method for optimal
subgraphs of the network, focusing the analysis on each part independently. We
also propose a new algorithm to speed up the computational cost of screening
the mesoscale looking for the resolution parameter that best splits every
subgraph. The hierarchical algorithm is able to solve a difficult benchmark
proposed in [Lancichinetti & Fortunato, 2011], encouraging the further analysis
of hierarchical methods based on the modularity quality function.
