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Efficient community detection via sampling processes in complex networks

Abstract. The identification of community 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, especially if the network under analysis is tightly connected.