Title:

ERGM parameter estimation of very large directed networks: implementation, example, and application to the geography of knowledge spillovers

Authors:

Alex Stivala (Universita della Svizzera italiana and Swinburne University of Technology)
Alfons Palangkaraya (Swinburne University of Technology)
Dean Lusher (Swinburne University of Technology)
Garry Robins (The University of Melbourne and Swinburne University of Technology)
Alessandro Lomi (Universita della Svizzera italiana and University of Exeter Business School)

Abstract:

The recently published Equilibrium Expectation (EE) algorithm for
exponential random graph model (ERGM) parameter estimation has allowed
such models to be estimated for networks far larger than previously
possible. Here we demonstrate the extension of this algorithm to
directed networks, with an implementation that overcomes some
technical problems limiting the sizes of networks that could be
practically estimated. We apply this method to estimate ERGM
parameters for an online social network with approximately 1.6 million
nodes, and a patent citation network with approximately 3.8 million
nodes. The latter model allows us to test the geographic knowledge
spillover hypothesis (that knowledge spillovers are geographically
localized) using patent citation data, without having to treat the
patent citation network as exogenous.