Cohesive Subgroup Analysis For Large-scale Interaction Data Sets

Recent years, every corporation in the communication realm has collected a large number of communication dataset of consumers along with the development of communication technology. The communication dataset is a very valuable resource which contains much useful information. If we can use effective techniques and methods to analyze the dataset, we will get much valuable knowledge.The thesis adopts an absolutely new method– social network analysis method– to analyze the communication dataset. We use the new method to analyze the communication network composed of consumers and communication relationships between them from the point of view of sociology. And this will provide much assistance in many fields such as decision support of enterprise and crime investigation.Social network analysis method is a quantitative sociology method. It takes a social actor as a vertex of a graph, and takes the relationship between two actors as an edge of a graph, and then use the algorithm of graph theory to resolve the problems of social network.The thesis designs a cohesive subgroup analysis model of large-scale communication dataset based on social network analysis method. And it finishes many substantial resolutions to the modules of data pretreatment, define of basic data structures, define of data interface, implement of pivotal graph algorithm, calculation of social network analysis centrality measures, cohesive subgroup analysis and data visualization. Many of the modules have been implemented by coding and validated by experimenting.