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In real-world social networks, there is an increasing interest in trackingthe evolution of groups of users and detecting the various changes they areliable to undergo. Several approaches have been proposed for this. In studyingthese approaches, we observed that most of them use a two-stage process. In thefirst stage, they run an algorithm to identify groups of users at eachtimestamp. In the second stage, a pair-wise comparison based on a similaritymeasure is employed to track groups of users and detect changes they mayundergo. While the majority of existing approaches use a two-stage process,they all run different algorithms to identify communities and rely on differentsimilarity measures to track groups of users over time. Noting that thedifferent approaches may perform differently depending on the dynamic socialnetwork under investigation, we decided to make a high level survey of someexisting tracking approaches and then do a comparative analysis of some ofthem. In our analysis, we compared the algorithms in two main situations: $(1)$when groups of users do not overlap and $(2)$ when the groups are overlapping.The study was done on three different testbeds extracted from the DBLP,Autonomous System (AS) and Yelp datasets.

A Comparative Study of Different Approaches for Tracking Communities in Evolving Social Networks