Detecting Structural Changes and Command Hierarchies in Dynamic Social Networks

Community detection in social networks varying with time is a common yet challenging problem whereby efficient visualization of evolving relationships and implicit hierarchical structure are important task. The main contribution of this paper is towards establishing a framework to analyze such social networks. The proposed framework is based on dynamic graph discretization and graph clustering.The framework allows detection of major structural changes over time, identifies events analyzing temporal dimension and reveals command hierarchies in social networks.We use the Catalano/Vidro dataset for empirical evaluation and observe that our framework provides a satisfactory assessment of the social and hierarchical structure present in the dataset.