An algorithmic approach to social networks

Abstract

Social networks consist of a set of individuals and some form of social relationship that ties the individuals together. In this thesis, we use algorithmic techniques to study three aspects of social networks: (1) we analyze the "small-world" phenomenon by examining the geographic patterns of friendships in a large-scale social network, showing how this linkage pattern can itself explain the small-world results; (2) using existing patterns of friendship in a social network and a variety of graph-theoretic techniques, we show how to predict new relationships that will form in the network in the near future; and (3) we show how to infer social connections over which information flows in a network, by examining the times at which individuals in the network exhibit certain pieces of information, or interest in certain topics. Our approach is simultaneously theoretical and data-driven, and our results are based upon real experiments on real social-network data in addition to theoretical investigations of mathematical models of social networks.