*********************************
There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
*********************************
Generative Models for Complex Network Structure
A Joint Seminar from the Center for Signal and Information Processing and Institute for Data and High Peformance Computing
Speaker:
Aaron Clauset from University of Colorado Boulder
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 are a central tool in understanding a wide range of biological, social, and technological phenomena.
Until recently, most work in network science focused on simple statistical measures, such as degree distributions and correlations, centrality measures, etc. These have yielded great insight but they capture only a fraction of the complexity of real-world networks. Increasingly, progress on important questions about the structure, function and dynamics of networks depends on going beyond these measures to identify and understand large-scale structural patterns, like modules and hierarchies, and to leverage vertex and edge annotations. Generative models and scalable inference algorithms provide a powerful, statistically principled and data-driven approach to solving these problems.
In this talk, I'll describe my recent work on generative models of modular and hierarchical organization in complex networks. Such organizational patterns, it turns out, can simultaneously explain many of the statistical regularities most commonly studied in networks, can generalize a single network to an ensemble of statistically similar networks, and can make accurate predictions about missing links. Importantly, these models can be extended to include arbitrary degree distributions, edge weights, latent spaces and network dynamics, which opens many new questions for analysis.
Biographical sketch:
Aaron Clauset is an Assistant Professor in the Department of Computer Science and the BioFrontiers Institute at the University of Colorado Boulder, where he is affiliated faculty in the Department of Ecology and Evolutionary Biology and the Department of Applied Mathematics. He is also External Faculty at the Santa Fe Institute. He received a PhD in Computer Science, with distinction, from the University of New Mexico, a BS in Physics, with honors, from Haverford College, and was an Omidyar Fellow at the prestigious Santa Fe Institute.
He is an internationally recognized expert on network science and computational analyses of complex systems. His work has appeared in prestigious scientific venues like Nature, Science, JACM, AAAI, ICML, STOC, SIAM Review, and Physical Review Letters, and has been covered in the popular press by the Wall Street Journal, The Economist, Discover Magazine, New Scientist, Wired, Miller-McCune, the Boston Globe and The Guardian.
