Learning From Networked-data: Methods and Models for Understanding Online Social Networks Dynamics

Abstract Nowadays, people and systems created by people are generating an unprecedented amount of data. This data has brought us data-driven services with a variety of applications that affect people’s behavior. One of these applications is the emergent online social networks as a method for communicating with each other, getting and sharing information, looking for jobs, and many other things. However, the tremendous growth of these online social networks has also led to many new challenges that need to be addressed. In this context, the goal of this thesis is to better understand the dynamics between the members of online social networks from two perspectives. The first perspective is to better understand the process and the motives underlying link formation in online social networks. We utilize external information to predict whether two members of an online social network are friends or not. Also, we contribute a framework for assessing the strength of friendship ties. The second perspective is to better understand the decay dynamics of online social networks resulting from the inactivity of their members. Hence, we contribute a model, methods, and frameworks for understanding the decay mechanics among the members, for predicting members’ inactivity, and for understanding and analyzing inactivity cascades occurring during the decay. The results of this thesis are: (1) The link formation process is at least partly driven by interactions among members that take place outside the social network itself; (2) external interactions might help reduce the noise in social networks and for ranking the strength of the ties in these networks; (3) inactivity dynamics can be modeled, predicted, and controlled using the models contributed in this thesis, which are based on network measures. The contributions and the results of this thesis can be beneficial in many respects. For example, improving the quality of a social network by introducing new meaningful links and removing noisy ones help to improve the quality of the services provided by the social network, which, e.g., enables better friend recommendations and helps to eliminate fake accounts. Moreover, understanding the decay processes involved in the interaction among the members of a social network can help to prolong the engagement of these members. This is useful in designing more resilient social networks and can assist in finding influential members whose inactivity may trigger an inactivity cascade resulting in a potential decay of a network