Prof. Harry Crane : Exchangeable Markov process models for time-varying networks

Posted by Jie Yang , part of the Statistics and Data Science Seminar.

At

Nov. 20, 2013, 4 p.m.

In

SEO 636

Abstract

In fields as diverse as physics, biology, sociology and national security, complex networks are used to model structural relationships among individuals and variables. In many applications, the networks vary over time and so are appropriately modeled by a stochastic process on the space of graphs. Motivated by these applications, we consider Markov processes that evolve on the space of infinite graphs. Natural statistical models for such processes are both exchangeable with respect to relabeling vertices and have the property that all restrictions to finite induced subgraphs are finite state space Markov chains. Our main theorem provides a Levy-Ito-type characterization for all processes in this class. Our approach also gives a straightforward recipe for simulating general processes of this type, which may be useful in a range of applications.