Keywords: There is much research on social network analysis but only recently did scholars turn their attention to the volatility of social networks. An abundance of questions emerged. How does a social network evolve – can we �nd laws and derive models that explain its evolution? How do communities emerge in a social network and how do they expand or shrink? What is a community in an evolving network – can we claim that two communities seen at two distinct timepoints are the same one, even if they have next to no members in common? Research advances have different perspectives: some scholars focus on how evolution manifests itself in a social network, while others investigate how individual communities evolve as new members join and old ones become inactive. There are methods for discovering communities and capturing their changes in time, and methods that consider a community as a smoothly evolving constellation and thus build and adapt models upon that premise. This survey organizes advances on evolution in social networks into a common framework and gives an overview of these different perspectives. Dynamic social networks, social network evolution, community evolution, stream clustering, incremental tensor-based clustering, dynamic probabilistic models 1.
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