Journal article
Snowball sampling for estimating exponential random graph models for large networks
AD Stivala, JH Koskinen, DA Rolls, P Wang, GL Robins
Social Networks | Published : 2016
Abstract
The exponential random graph model (ERGM) is a well-established statistical approach to modelling social network data. However, Monte Carlo estimation of ERGM parameters is a computationally intensive procedure that imposes severe limits on the size of full networks that can be fitted. We demonstrate the use of snowball sampling and conditional estimation to estimate ERGM parameters for large networks, with the specific goal of studying the validity of inference about the presence of such effects as network closure and attribute homophily. We estimate parameters for snowball samples from the network in parallel, and combine the estimates with a meta-analysis procedure. We assess the accuracy..
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Awarded by National Science Foundation
Funding Acknowledgements
This research was supported by a Victorian Life Sciences Computation Initiative (VLSCI) grant numbers VR0261 and VR0297 on its Peak Computing Facility at the University of Melbourne, an initiative of the Victorian Government, Australia. This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1053575. Specifically, we used the Gordon Compute Cluster at SDSC under allocation TG-SES140024 "Exponential Random Graph Models for Large Networks: Snowball Sampling and Conditional Estimation using Parallel High Performance Computing". We also used the University of Melbourne ITS High Performance Computing facilities. Johan Koskinen acknowledges financial support through the Leverhulme Trust (RPG-2013-140) and British Academy/Leverhulme (SG121127).