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Many systems can be described using graphs, or networks. Detectingcommunities in these networks can provide information about the underlyingstructure and functioning of the original systems. Yet this detection is acomplex task and a large amount of work was dedicated to it in the past decade.One important feature is that communities can be found at several scales, orlevels of resolution, indicating several levels of organisations. Thereforesolutions to the community structure may not be unique. Also networks tend tobe large and hence require efficient processing. In this work, we present a newalgorithm for the fast detection of communities across scales using a localcriterion. We exploit the local aspect of the criterion to enable parallelcomputation and improve the algorithm's efficiency further. The algorithm istested against large generated multi-scale networks and experiments demonstrateits efficiency and accuracy.

Fast Multi-Scale Community Detection based on Local Criteria within a Multi-Threaded Algorithm