Abstract-We develop an algorithmic framework for studying the evolution of communities in social networks. We begin with the theoretical foundation, from which we conclude that the evolution is at most as strong as its weakest link. This allows us to deign an efficient algorithm which identifies all evolutionary sequences in a dynamic social network. We use this algorithm to empirically study community evolution in several large social networks, and in particular, to identify those features of the early stages of a community that indicate whether a community is going to be short-lived or not. Our results show that it is possible to correlate the lifespan of a community with structural parameters of its early evolution; these conclusions are robust across all the social networks that we have investigated.
In REF , structural information extracted from the early stages of a community were utilized to infer the lifespan of a community using linear regression.
Tracking and Predicting Evolution of Social Communities
2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing
2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing
Computer Science