Overlapping community detection based on conductance optimization in large-scale networks

Community structure reveals useful information in domains of sociology, biology, physics and computer science. In this work, an overlapping community detection algorithm for large-scale networks based on local expansion is proposed, in which we present a novel seeding method. And we optimize conductance of communities by: (1) modifying inaccurate community affiliations by node movements; (2) combining densely overlapping communities with a novel combining function; (3) finding communities for the outliers with our proposed theorem. Experimental results in synthetic networks show that the optimization largely enhance the community accuracy. Experimental results in large real-world networks show that our approach is superior to the others in the state of the art.