In this paper, we attempt to detect the changes in the structure of an
evolving social network. We define a novel measure to quantify the dynamics
of the network and use it to generate a timeline for the network that indicates
the changes. We consider that any significant change in the network is as
a result of occurrence of some event, and thus, identify the day or time step
when the event took place. Further, the analysis involves identification of
key individuals and their close associates that were active on that day. Finally,
the case study involves identification of individuals having communication
behaviour similar to the key individuals. To group such individuals, we use
a recently proposed online clustering approach called evolving clustering
(eClustering). We demonstrate the efficacy of the proposed quantification
measures by conducting several experiments and comparing the results with the
ground truth.