Networks are dyadic data structures. They are “social” when the nodes are (occupied by) agents with heterogeneous preferences, strategies, and capabilities shaped by a combination of (i) Their attributes and endowments; (iii) Positions they occupy in flows of information and resources, and (iii) Outcomes of the interactions they choose to initiate, change, and abandon. INF 524 introduces a wide range of descriptive, inferential and model-based approaches for the representation and analysis of various kinds of social networks. During the course we will discuss recent scientific papers that illustrate the application of network-analytic concepts to classic problems in social science, and to current problems in contemporary computational social science. Participants will be encouraged to propose and carry out their own individual research project involving the design and implementation of an empirical study to replicate or extend core research results that will be discussed in class.