Fritz, C, Mehrl, M orcid.org/0000-0002-5825-9256, Thurner, PW et al. (1 more author) (2023) All that glitters is not gold: Relational events models with spurious events. Network Science, 11 (Special Issue 2). pp. 184-204. ISSN 2050-1242
Abstract
As relational event models are an increasingly popular model for studying relational structures, the reliability of large-scale event data collection becomes more and more important. Automated or human-coded events often suffer from non-negligible false-discovery rates in event identification. And most sensor data are primarily based on actors’ spatial proximity for predefined time windows; hence, the observed events could relate either to a social relationship or random co-location. Both examples imply spurious events that may bias estimates and inference. We propose the Relational Event Model for Spurious Events (REMSE), an extension to existing approaches for interaction data. The model provides a flexible solution for modeling data while controlling for spurious events. Estimation of our model is carried out in an empirical Bayesian approach via data augmentation. Based on a simulation study, we investigate the properties of the estimation procedure. To demonstrate its usefulness in two distinct applications, we employ this model to combat events from the Syrian civil war and student co-location data. Results from the simulation and the applications identify the REMSE as a suitable approach to modeling relational event data in the presence of spurious events.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s), 2022. This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by/4.0/) |
Keywords: | data augmentation; longitudinal network analysis; relational event model; spurious events |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Education, Social Sciences and Law (Leeds) > School of Politics & International Studies (POLIS) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 24 Oct 2022 11:31 |
Last Modified: | 19 Oct 2023 15:35 |
Status: | Published |
Publisher: | Cambridge University Press |
Identification Number: | 10.1017/nws.2022.22 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:191994 |
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