Weighted Exponential Random Graph Models: Scope and Large Network Limits

Shankar Bhamidi, Suman Chakraborty, Skyler Cranmer, Bruce Desmarais

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)704-735
Number of pages32
JournalJournal of Statistical Physics
Volume173
Issue number3-4
DOIs
StatePublished - Nov 1 2018

All Science Journal Classification (ASJC) codes

  • Statistical and Nonlinear Physics
  • Mathematical Physics

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