  The identification of modular structures is essential for characterizing real
networks formed by a mesoscopic level of organization where clusters contain
nodes with a high internal degree of connectivity. Many methods have been
developed to unveil community structures, but only a few studies have probed
their suitability in incomplete networks. Here we assess the accuracy of
community detection techniques in incomplete networks generated in sampling
processes. We show that the walktrap and fast greedy algorithms are highly
accurate for detecting the modular structure of incomplete complex networks
even if many of their nodes are removed. Furthermore, we implemented an
approach that improved the time performance of the walktrap and fast greedy
algorithms, while retaining the accuracy rate in identifying the community
membership of nodes. Taken together our results show that this new approach can
be applied to speed up virtually any community detection method in dense
complex networks, as it is the case of similarity networks.
