  Understanding the information behind social relationships represented by a
network is very challenging, especially, when the social interactions change
over time inducing updates on the network topology. In this context, this paper
proposes an approach for analysing dynamic social networks, more precisely for
Twitter's network. Our approach relies on two complementary steps: (i) an
online community identification based on a dynamic community detection
algorithm called Dyci. The main idea of Dyci is to track whether a connected
component of the weighted graph becomes weak over time, in order to merge it
with the "dominant" neighbour community. Additionally, (ii) a community
visualization is provided by our visualization tool called NLCOMS, which
combines between two methods of dynamic network visualization. In order to
assess the efficiency and the applicability of the proposed approach, we
consider real-world data of the ANR-Info-RSN project, which deals with
community analysis in Twitter.
