Community structure analysis is a critical task in examining a complex network. This paper presented a self-organizing overlapping community structure analysis algorithm(SO²CSA²) based on the swarm intelligence theory. The basic idea behind the algorithm was that it treats an analyzed network as a swarm intelligence system, of which each node was an individual with simple intelligence. Each individual independently decides to which community it joined based on a defined metric named connection score. An individual could join to multiple communities simultaneously. At first, the algorithm found a set of K-cliques from the analyzed network as the initial community structure. Then, each individual in the system acted iteratively to join into or leave from communities, and an optimal community structure of the whole network could develop and eventually emerge from the initial community structure. Finally, to improve the quality of the obtained community structure, a post process adjusted community assignments of a small number of individuals. Experimental evaluation on a number of synthesized networks and real-world networks indicates that the quality of community structures discovered by SO²CSA² is better than those of two compared algorithms, SLPA and OSLOM, especially on networks with a large number of overlapping nodes or on networks with overlapping nodes of which overlapping degrees are high.

[2019-05-05] Printed Article /doc/2017.11.0733/v2