  In real-world social networks, there is an increasing interest in tracking
the evolution of groups of users and detecting the various changes they are
liable to undergo. Several approaches have been proposed for this. In studying
these approaches, we observed that most of them use a two-stage process. In the
first stage, they run an algorithm to identify groups of users at each
timestamp. In the second stage, a pair-wise comparison based on a similarity
measure is employed to track groups of users and detect changes they may
undergo. While the majority of existing approaches use a two-stage process,
they all run different algorithms to identify communities and rely on different
similarity measures to track groups of users over time. Noting that the
different approaches may perform differently depending on the dynamic social
network under investigation, we decided to make a high level survey of some
existing tracking approaches and then do a comparative analysis of some of
them. 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.
