Research Statement Hossam Sharara Doctoral Research Network Evolution Bi-modal Interaction Dynamics

My research interests span the areas of data mining, machine learning, and social network analysis. A common thread in my research is understanding the different types of interactions that occur within social networks, and their effects on the users’ behavior. In particular, my research focuses on reasoning in dynamic, multi-modal, multi-relational network settings, where there exist multiple entity types (such as people, social groups, organizations, locations, etc.), and different types of relationships (such as friendship, membership, affiliation, etc.). For example, in social network settings, in addition to friendship relationships, users can have family ties with other users, join multiple social groups, be affiliated with various organizations, or belong to different geographical sub-networks. By considering the complex structure of these networks rather than limiting the analysis to a single entity or relationship type, we can build richer models that provide better understanding of the dynamics in these settings, and thus help in providing highly accurate predictions about future events. My long-term research goals are to analyze a number of aspects related to information networks (such as group formation, network clustering, information diffusion, etc.), create explanatory and predictive models of the interactions that occur in complex network settings, and investigate the implications of these studies on applications in different domains. Based on my doctoral research, I believe that capturing different types of dependencies that exist in networked systems is a propitious approach for understanding the dynamics of the corresponding domains, particularly human behavior and actions in social context.