1 research outputs found
Weighted Exponential Random graph models: Scope and large network limits
We study models of weighted exponential random graphs in the large network
limit. These models have recently been proposed to model weighted network data
arising from a host of applications including socio-econometric data such as
migration flows and neuroscience. Analogous to fundamental results derived for
standard (unweighted) exponential random graph models in the work of Chatterjee
and Diaconis, we derive limiting results for the structure of these models as
the number of nodes goes to infinity. Our results are applicable for a wide
variety of base measures including measures with unbounded support. We also
derive sufficient conditions for continuity of functionals in the specification
of the model including conditions on nodal covariates. Finally we include a
number of open problems to spur further understanding of this model especially
in the context of applications.Comment: 27 page