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Most existing approaches for community detection require complete informationof the graph in a specific scale, which is impractical for many socialnetworks. We propose a novel algorithm that does not embrace the universalapproach but instead of trying to focus on local social ties and modelingmulti-scales of social interactions occurring in those networks. Our method forthe first time optimizes the topological entropy of a network and uncoverscommunities through a novel dynamic system converging to a local minimum bysimply updating the membership vector with very low computational complexity.It naturally supports overlapping communities through associating each nodewith a membership vector which describes node's involvement in each community.This way, in addition to uncover overlapping communities, we can also describedifferent multi-scale partitions by tuning the characteristic size of modulesfrom the optimal partition. Because of the high efficiency and accuracy of thealgorithm, it is feasible to be used for the accurate detection of communitystructure in real networks.

Identifying overlapping communities in social networks using multi-scale local information expansion