gwdegree: Improving interpretation of geometrically-weighted degree estimates in exponential random graph models
Michael A Levy · The Journal of Open Source Software · 2016
Exponential random graph models (ERGMs) are maximum entropy statistical models that provide estimates on network tie formation of variables both exogenous (covariate) and endogenous (structural) to a network.Network centralization -the tendency for edges to accrue among a small number of popular nodes -is a key network variable in many fields, and in ERGMs it is primarily modeled via the geometrically-weighted degree (GWD) statistic (Snijders et al. 2006;Hunter 2007).However, the published literature is ambiguous about how to interpret GWD estimates, and there is little guidance on how to interpret or fix values of the GWD shape-parameter, θ S .This Shiny application seeks to improve the use of GWD in ERGMs by demonstrating: