Computing That Serves

Colloquium: Modeling the Large-Scale Structure of Complex Networks


Thursday, March 6, 2014 - 11:00am


Aaron Clauset


Christophe Giraud-Carrier

Networks have become a powerful tool for studying complex systems: they provide an abstraction of 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 social, biological, and technological phenomena. 

Until recently, most work in network science focused on simple statistical measures, such as degree distributions and degree 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.


Aaron Clauset is an Assistant Professor in the Department of Computer Science and the BioFrontiers Institute at the University of Colorado Boulder, and is 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, data science, and complex systems. His work has appeared in prestigious scientific venues like Nature, Science, PNAS, JACM, AAAI, ICML, SIAM Review, and Physical Review Letters, and has been covered in the popular press by the Wall Street Journal, The Economist, Slate Magazine, Discover Magazine, New Scientist, Wired, Miller-McCune, the Boston Globe and The Guardian.