Evolutionary community structure discovery in dynamic weighted networks

Detecting evolutionary community structure in dynamic weighted networks is important for understanding the structure and functions of networks. In this paper, an algorithm which considers the historic community structure of networks is developed to detect evolutionary community structure in dynamic weighted networks. In the proposed algorithm, two measures are proposed to determine whether to add a node to a community and whether to merge two communities to form a new community. The proposed algorithm can automatically discover evolutionary community structure in weighted networks whose number of nodes and communities is changing over time and does not need to determine the number of communities in advance. The algorithm is tested using a synthetic network and two real-word complex networks. Experimental results demonstrate that the proposed algorithm can discover evolutionary community structure in dynamic weighted networks effectively.