News and Events

Colloquia

Speaker

Aaron Clauset

When

Thursday, May 7

Where

Workman 101

Time

4pm

Topic

Inferring large-scale structural patterns in complex networks

Abstract

Networks have become a powerful tool for studying complex systems: they provide an abstraction of a system's interacting parts that is both general enough to encompass important features of real systems and simple enough to offer clear insights and general results. Already, networks have become a central tool in understanding a wide range of biological, social, and technological phenomena.

Until recently, most work on networks focused on simple statistical regularities, such as degree distributions and correlations, centrality measures, etc. These measures do yield some insight, but they capture only a fraction of the complexity of real-world networks. Increasingly, progress on important questions of structure and function depend on going beyond these measures to understand theorigins and functional significance of large-scale structural patterns, such as modules and hierarchies.

In this talk, I'll briefly describe my work developing algorithms to automatically infer these large-scale patterns. Using the example of hierarchical structure, I'll describe a flexible generative model and a principled technique for inferring it directly from network data. A hierarchical organization, it turns out, can simultaneously explain many of the simple statistical regularities mentioned above, as well as generalize a single network to an ensemble of statistically similar networks, and make highly accurate predictions about missing links.

See the full colloquium schedule for other seminars this semester.