Mark Handcock (UCLA), le 28 juin 2019

vendredi 28 juin 2019

Résumé : Random graphs, where the connections between nodes are considered random variables, have wide applicability in the social sciences. Exponential-family Random Graph Models (ERGM) have shown themselves to be a useful class of models for representing complex social phenomena.

In this talk we will consider some new classes of models that generalize ERGM in different ways. We consider Exponential-family Random Network Models (ERNM), Tapered Exponential-family Random Network Models (TERNM) and spatial temporal exponential-family of point processes (STEPP) models to jointly represent the co-evolution of social relations and individual behavior in discrete time.

This is joint work with Ian E. Fellows and Joshua D. Embree .