Mark Newman (University of Michigan)
Large-scale structure in networks
Networks are of broad interest in physics, biology, engineering, and many other areas for the light they shed on the shape and function of complex systems. This talk will focus on the large-scale structure of networked systems — what do they look like when you stand back and take in the whole network? This is a difficult question to answer because in most cases the networks we study are too large and complicated to allow us to actually make a picture of them. Two promising classes of methods for understanding structure are spectral methods and inference methods. This talk will demonstrate some applications of both and discuss some beautiful recent results that link the two together and reveal deep and unexpected truths about large-scale structure in networks.
Hierarchical structure and the prediction of missing links in networks, Aaron Clauset, Cristopher Moore, and M. E. J. Newman, Nature 453, 98–101 (2008)
Mixture models and exploratory analysis in networks, M. E. J. Newman and E. A. Leicht, Proc. Natl. Acad. Sci. USA 104, 9564–9569 (2007)
Graph spectra and the detectability of community structure in networks, Raj Rao Nadakuditi and M. E. J. Newman, Phys. Rev. Lett. 108, 188701 (2012)