Dr. Ashwin Bahulkar supervised by Prof. Bolesaw K. Szymanski defended the Ph.D. thesis "Evolution Dynamics of Attribute-Rich Social Networks" and will join Facebook in August 2019.

Dr. Ashwin Bahulkar supervised by Prof. Bolesaw K. Szymanski defended the Ph.D. thesis on "Evolution Dynamics of Attribute-Rich Social Networks" and will join Facebook in August 2019. In the thesis the author study the evolution dynamics in attribute-rich social networks. First, he demonstrates that node attributes can be used to predict the formation and dissolution of new links in these networks, using node preferences for different attributes. In the university based social network studied in the thesis, personal preferences, in particular for political views and preferences for common activities help predict link formation and dissolution. We then we show that link prediction can be used to identify changes in network stability on the sample application to collaboration networks. We then examine the dynamics of coevolution of three-layer node-aligned network with nominations layer based on perceived prominence collected from repeated surveys of students, one behavioral layer based on communication via mobile phones and second behavioral layer based on interactions implied by Bluetooth collocations. We investigate how these layers co-evolve and explore the causes of observed temporal dependencies between the three layers, We also measure the predictive capacity of such dependencies. We further explore the evolution of groups in social networks. We examine how group formation differs from tie-formation in terms of the role of selectivity based on opinions and attributes. We then explore the interdependence of opinion change and social group membership in human social networks. We look at group joining and leaving and opinion change as
a set of mutually reinforcing behaviors. We propose a benefi t-driven mathematical model and a related machine learning based approach designed to predict the changes that people
will make. Our model is able to predict observed opinion changes in the data with relatively high accuracy. We also explore how having an opinion similar to the majority of members of the group affects the overall benefi t a person's derives from social interactions.