Construction of and efficient sampling from the simplicial configuration model

Friday, Oct 20th 2017 at 4:00 PM
Informatics East 122

Jean-Gabriel Young

Département de Physique, de GéniePhysique, etd'Optique

Université Laval


Construction of and efficient sampling from the simplicial configuration model



Abstract: It has been shown recently that the structure of complex systems is not always correctly represented by networks, due to the presence of many-body interactions. An increasingly popular alternative is to instead encode these interactions explicitly, using simplicial complexes (a generalization of graphs). With this new solution comes the need for principled null models. Drawing inspiration from the network literature, we propose a natural candidate: the simplicial configuration model.

In this talk, I will present the model, as well as its associated efficient and uniform Markov chain Monte Carlo sampler. I will demonstrate its usefulness by investigating the relationship between the actual and randomized Betti numbers of a few real systems. This will allows us to conclude---based on sound statistical arguments---that the structure of some systems is essentially random, while large-scale organizational principle intervene in others.

Biography: Jean-Gabriel Young is a Ph.D candidate in Physics and Network Science, working under the guidance of Prof. Louis J. Dubé and Prof. Patrick Desrosiers at Université Laval, Québec, (QC), Canada. He studies the classical theory of complex systems and complex networks. His focus is the intersection between these topics and rigorous statistical and computational methods. He made contributions to subjects ranging from community detection and inference in complex networks, to epidemiology, game theory, and stochastic growth process.