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Reliability of relational event model estimates under sampling: How to fit a relational event model to 360 million dyadic events

Published online by Cambridge University Press:  22 November 2019

Jürgen Lerner Open the ORCID record for Jürgen Lerner [Opens in a new window]  and
Alessandro Lomi
Jürgen Lerner*
Affiliation:
Department of Computer and Information Science, University of Konstanz, Konstanz, Germany
Alessandro Lomi
Affiliation:
University of Italian Switzerland, Lugano, Switzerland University of Exeter Business School, Exeter, UK
*
*Corresponding author. Email: [email protected]
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Abstract

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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.

Keywords

relational event modelsdynamic networkslarge networksparameter estimationsamplingWikipedia
Type
Research Article
Information
Network Science , Volume 8 , Issue 1 , March 2020 , pp. 97 - 135
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© Cambridge University Press 2019

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