Reliability of relational event model estimates under sampling: How to fit a relational event model to 360 million dyadic events. (22nd March 2020)

• Record Type: Journal Article
• Authors: Lerner, Jürgen; Lomi, Alessandro
• Is Part Of: Network science. Volume 8:Number 1(2020)
• Page Start: 97
• Page End: 135
• Publication Date: 2020-03-22
• Subjects: relational event models, -- dynamic networks, -- large networks, -- parameter estimation, -- sampling, -- Wikipedia; Social networks -- Research -- Periodicals; System analysis -- Periodicals; System theory -- Periodicals; Computer science -- Periodicals; 003.72
• DOI: 10.1017/nws.2019.57
• Languages: English
• ISSNs: 2050-1242

Abstract

Abstract: We assess the reliability of relational event model (REM) parameters estimated under two sampling schemes: (1) uniform sampling from the observed events and (2) case–control sampling which samples nonevents, or null dyads ("controls"), from a suitably defined risk set. We experimentally determine the variability of estimated parameters as a function of the number of sampled events and controls per event, respectively. Results suggest that REMs can be reliably fitted to networks with more than 12 million nodes connected by more than 360 million dyadic events by analyzing a sample of some tens of thousands of events and a small number of controls per event. Using the data that we collected on the Wikipedia editing network, we illustrate how network effects commonly included in empirical studies based on REMs need widely different sample sizes to be reliably estimated. For our analysis we use an open-source software which implements the two sampling schemes, allowing analysts to fit and analyze REMs to the same or other data that may be collected in different empirical settings, varying sample parameters or model specification.