  Detection of community structures in social networks has attracted lots of
attention in the domain of sociology and behavioral sciences. Social networks
also exhibit dynamic nature as these networks change continuously with the
passage of time. Social networks might also present a hierarchical structure
led by individuals that play important roles in a society such as Managers and
Decision Makers. Detection and Visualization of these networks changing over
time is a challenging problem where communities change as a function of events
taking place in the society and the role people play in it.
  In this paper we address these issues by presenting a system to analyze
dynamic social networks. The proposed system is based on dynamic graph
discretization and graph clustering. The system allows detection of major
structural changes taking place in social communities over time and reveals
hierarchies by identifying influential people in a social networks. We use two
different data sets for the empirical evaluation and observe that our system
helps to discover interesting facts about the social and hierarchical
structures present in these social networks.
