关键词: 社区结构, 人工鱼群算法, 标签传播, 模块度

Abstract:

With the phenomenon of small business big data emerged, “complex networks as complex system model” has been very popular. Community detection is one of the most important issues. But the existing community detection algorithms mostly assume that no overlaps exist. Aimed at the common phenomenon of overlapping community, an overlapping community detection algorithm, named AFSCDA, is proposed based on fishschool algorithm. In the initialization phase, a label propagation algorithm is utilized on optimization variables of each artificial fish for coding adjustment, trying to avoid illegal community. We will apply the deformation module of the Q function as the fitness function. In experiments, the algorithm is applied to three classic datasets with known community structures in order to demonstrate the algorithm's effectiveness, higher accuracy, capability of detecting the potential community structure quickly in networks.

Key words: community structure;artificial fishschool algorithm;label propagation;modularity