Large networks have become increasingly popular over the last decades, and their modeling and investigation have led to interesting and new ways to apply analytical and statistical methods. The introduction of exponential random graphs has aided in this pursuit, as they are able to capture a wide variety of common network tendencies by representing a complex global configuration through a set of tractable local features. This talk will look into the asymptotic structure and dynamics of weighted exponential random graphs and formulate a quantitative theory of phase transitions. The main techniques that we use are variants of statistical physics (both equilibrium and non-equilibrium). Based on joint work with multiple collaborators.
Mei Yin is an Assistant Professor in mathematics at the University of Denver. She obtained her Ph.D. in mathematics (minor in statistics) at the University of Arizona, and then held postdoctoral appointments at the University of Texas at Austin and Brown University. Her main research areas are probability and mathematical physics, with an emphasis on statistical physics. Her work has been supported by grants from the NSF and the Combinatorics Foundation.
Seminar by the NYU-ECNU Institute of Mathematical Sciences at NYU Shanghai