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In this paper a simple but efficient real-time detecting algorithm isproposed for tracking community structure of dynamic networks. Communitystructure is intuitively characterized as divisions of network nodes intosubgroups, within which nodes are densely connected while between which theyare sparsely connected. To evaluate the quality of community structure of anetwork, a metric called modularity is proposed and many algorithms aredeveloped on optimizing it. However, most of the modularity based algorithmsdeal with static networks and cannot be performed frequently, due to their highcomputing complexity. In order to track the community structure of dynamicnetworks in a fine-grained way, we propose a modularity based algorithm that isincremental and has very low computing complexity. In our algorithm we adopt atwo-step approach. Firstly we apply the algorithm of Blondel et al fordetecting static communities to obtain an initial community structure. Then,apply our incremental updating strategies to track the dynamic communities. Theperformance of our algorithm is measured in terms of the modularity. We testthe algorithm on tracking community structure of Enron Email and three otherreal world datasets. The experimental results show that our algorithm can keeptrack of community structure in time and outperform the well known CNMalgorithm in terms of modularity.

A Real-Time Detecting Algorithm for Tracking Community Structure of Dynamic Networks