Data Analytics and Computation 790N - Network Inference

This course covers various approaches for network inference and delves into the following questions: How do networks we observe emerge? Under what conditions do they change (or not)? What are the network outcomes individuals get based on their structural positions and roles? How do information and resources move from one spot to another within and beyond networks? Where does the flow stop? What are the mechanisms that lead to changes to networks? Do people form (or dissolve) social relations because of shared similarities (or outright differences) with others, or because they tend to be influenced by others already in their networks? Extending the discussion on descriptive and structural characteristics of network data in Social Network Analysis (DACSS 695N), this course introduces statistical frameworks with which network dynamics can be investigated. Recapitulating issues that make it hard to marry conventional frameworks for statistical inference with network data, such as autocorrelation and relational dependency, this course starts with mathematical models that utilize some mechanisms of network formation and continues on to statistical models. In particular, this course introduces some well-known statistical network models such as exponential random graph models (ERGMs), network regression models, latent space models, and stochastic actor-oriented model (SAOMs), as well as models that consider temporality in statistical frameworks such as temporal exponential random graph models (TERGMs) and diffusion in networks.