Improving Community Detection Methods for Network Data Analysis

Empirical analysis of network data has been widely conducted for understanding and predicting the structure and function of real systems and identifying interesting patterns and anomalies. One of the most widely studied structural properties of networks is their community structure. In this thesis we investigate some of the challenges and applications of community detection for analysis of network data and propose different approaches for improving community detection methods. One of the challenges in using community detection for network data analysis is that there is no consensus on a definition for a community despite excessive studies which have been performed on the community structure of real networks. There-fore, evaluating the quality of the communities identified by different community detection algorithms is problematic. In this thesis, we perform an empirical com-parison and evaluation of the quality of the communities identified by a variety of community detection algorithms which use different definitions for communi-ties for different applications of network data analysis. Another challenge in usin