A Novel Clonal Selection Algorithm for Community Detection in Complex Networks

摘要

Recent years have seen the arising recognition of community detection in complex networks. Artificial immune systems, owing to their inherent properties, have been thoroughly studied and well applied to practical use. In this article, one of the well-known artificial immune system models, named clonal selection algorithm, is introduced to reveal community structures in complex networks. By introducing a novel antibody population initialization mechanism and a novel hypermutation strategy, the proposed approach could be applied to moderate-scale network. Besides, by optimizing an objective function called modularity density, the proposed algorithm is also capable of detecting community structure at multiple resolution levels. Experiments on both synthetic and real-world networks demonstrate the effectiveness of the proposed method.