  Aiming at improving the efficiency and accuracy of community detection in
complex networks, we proposed a new algorithm, which is based on the idea that
communities could be detected from subnetworks by comparing the internal and
external cohesion of each subnetwork. In our method, similar nodes are firstly
gathered into meta-communities, which are then decided to be retained or merged
through a multilevel label propagation process, until all of them meet our
community criterion. Our algorithm requires neither any priori information of
communities nor optimization of any objective function. Experimental results on
both synthetic and real-world networks show that, our algorithm performs quite
well and runs extremely fast, compared with several other popular algorithms.
By tuning a resolution parameter, we can also observe communities at different
scales, so this could reveal the hierarchical structure of the network. To
further explore the effectiveness of our method, we applied it to the E-Coli
transcriptional regulatory network, and found that all the identified modules
have strong structural and functional coherence.
