Algorithms for Identifying Dynamic Communities

We propose an approximation algorithm for identifying communities in dynamic social networks. Communities are intuitively characterized as “densely knit” subsets of a social network. This notion becomes more problematic if the social interactions change over time. Aggregating social networks over time can radically misrepresent the existing and changing community structure. Recently, we have proposed an optimization-based approach for modeling dynamic community structure. Also we have proposed an algorithm for finding such structure based on maximum weight bipartite matching. In this paper, we extend this algorithm and analyze its performance guarantee for a special case where all actors can be observed at all times. In such instances, we show that the algorithm is a small constant factor approximation of the optimum. In addition, we present an approximation algorithm for the general case where some individuals are possibly unobserved at times and show that the approximation factor increases twofold but remains a constant. This is the first algorithm for inferring communities in dynamic networks with a provable approximation guarantee. We demonstrate the general algorithm on real data sets. The results confirm the efficiency and effectiveness of the algorithm in identifying dynamic communities.