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arXiv · 2609.06713

Tracking and Predicting Evolution of Social Communities

Mark Goldberg·Malik Magdon-Ismail·Srinivas Nambirajan·James Thompson
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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 an evolution is at most as strong as its weakest link. This allows us to formulate an efficient algorithm to identify all evolutionary sequences in a dynamic social network. We use this algorithm to empirically study community evolution in several large social networks, to identify those features of the early stages of a community that indicate whether a community is going to be shortlived or not. Our results show that it is possible to correlate the lifespan of a community to structural parameters of its early evolution; these conclusions are robust across all the social networks we have investigated.

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Mark Goldberg, Malik Magdon-Ismail, Srinivas Nambirajan, James Thompson. 2026-09-06. Tracking and Predicting Evolution of Social Communities. https://doi.org/10.1109/passat/socialcom.2011.102

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