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

Physics and Society;Social and Information Networks