Statistics and Data Science Seminar
Prof. Harry Crane
Rutgers University
Exchangeable Markov process models for time-varying networks
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.
Wednesday November 20, 2013 at 4:00 PM in SEO 636