Abstract
The inherent knowledge discovery problem regarding networks that represent complex real world phenomenon is a popular research topic. Specifically, in social network analysis (SNA), several community discovery techniques with various approaches have been put forward to distinguish closely related entities. Identifying the relevant techniques to utilize based on the context of the application is a key difficulty researchers face. In this study we propose a methodology for classifying these techniques, visualize a prototype, and analyze the performance and quality of selected approaches over a real world call detail record (CDR) data set.
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