Abstract: A fundamental problem in the study of networked systems is how to characterize their structure. Given a large and unwieldy data set -- gigabytes of data or more -- can we come up with a simple and useful description of the network it contains? Historically, work on network structure has focused on two different scales: small-scale structure, represented by properties such as degrees, correlations, and clustering, and large-scale structure, which is most commonly presented in terms of modules and community detection. In this talk I will focus on large-scale structure, but with the aim of getting away from community structure, which is well-trodden ground, and looking at other forms. Working with generative models and a range of model-based inference techniques, I'll talk about overlapping communities, hierarchical structure, latent-space structure, ranking, and core-periphery structure, among others, and give illustrative examples, primarily drawn from studies of social networks.
Bio: Professor Newman is the Anatol Rapoport Distinguished University Professor of Physics at the University of Michigan. His research is on statistical physics and the theory of complex systems, with a primary focus on networked systems, including social, biological, and computer networks, which are studied using a combination of empirical methods, analysis, and computer simulation. Among other topics, he and his collaborators have worked on mathematical models of network structure, computer algorithms for analyzing network data, and applications of network theory to a wide variety of specific problems, including the spread of disease through human populations and the spread of computer viruses among computers, the patterns of collaboration of scientists and business-people, citation networks of scientific articles and law cases, network navigation algorithms and the design of distributed databases, and the robustness of networks to the failure of their nodes. Professor Newman is the author of several books, including a recent textbook on network theory and a popular book of cartography.