Overlapping community detection using expansion with contraction

Numerous disjoint community detection methods have reached the state-of-the-art. Some overlapping community detection methods have been proposed in recent years, but they lack the ability to adjust the degree of overlap while maintaining detection quality. To well handle this issue, we in this paper propose a novel method, namely expansion with contraction method for overlapping community detection (ECOCD). Specifically, ECOCD obtains the disjoint communities through non-negative matrix factorization and proceeds to expansion with contraction process (including the expansion process and the contraction process). In each iteration of the process, we randomly select a community and then continuously conduct the expansion and contraction processes on this community. The former process absorbs nodes by the degree of affiliation that is newly defined, while the latter removes nodes by permanence. Moreover, we theoretically analyze the computational complexity of ECOCD. The advantage of ECOCD is that it is applicable to various networks with different properties by adjusting the degree of overlap, and enjoys high quality of overlapping community detection as well. Our experiments on both synthetic and real-world networks further verify this. Extensive experiments show that ECOCD is superior to the eleven state-of-the-art overlapping community detection methods in terms of four metrics, validating the effectiveness, efficiency and robustness of ECOCD.