Constant-factor approximation algorithms for identifying dynamic communities

We propose two approximation algorithms for identifying communities in dynamic social networks. Communities are intuitively characterized as "unusually 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 framework 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 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. We use a similar idea to design an approximation algorithm for the general case where some individuals are possibly unobserved at times, and to show that the approximation...

• Type: Conference
• Year: 2009
• Where: KDD
• Authors: Chayant Tantipathananandh, Tanya Y. Berger-Wolf
• Keywords: Approximation Algorithm, Constant Factor Approximation, Data Mining, KDD 2009, Provable Approximation Guarantee