paper/paper.md

In michaellevy/gwdegree: A Shiny App to Aid Interpretation of Geometrically-Weighted Degree Estimates in Exponential Random Graph Models

title: 'gwdegree: Improving interpretation of geometrically-weighted degree estimates in exponential random graph models' bibliography: paper.bib date: "05 July 2016" output: pdf_document tags: - social network analysis - ERGM - R authors: - affiliation: University of California, Davis; Department of Environmental Science and Policy name: Michael A Levy orcid: 0000-0002-4188-2527

Summary

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_new_2006; @hunter_curved_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, $\theta_S$. This Shiny application seeks to improve the use of GWD in ERGMs by demonstrating:

1. how the GWD statistic responds to adding edges to nodes of various degrees, contingent on the value of the shape parameter, $\theta_S$;

2. how the degree distribution of networks of various size and density are shaped by GWD parameter and $\theta_S$ values;

3. how GWD and GWESP -- an ERGM term used to model triadic closure -- interact to affect network centralization and clustering.

References

michaellevy/gwdegree documentation built on May 22, 2019, 9:51 p.m.