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Annual Review of Statistics and Its Application

Volume 11, 2024

Relational Event Modeling

Abstract

Advances in information technology have increased the availability of time-stamped relational data, such as those produced by email exchanges or interaction through social media. Whereas the associated information flows could be aggregated into cross-sectional panels, the temporal ordering of the events frequently contains information that requires new models for the analysis of continuous-time interactions, subject to both endogenous and exogenous influences. The introduction of the relational event model (REM) has been a major development that has stimulated new questions and led to further methodological developments. In this review, we track the intellectual history of the REM, define its core properties, and discuss why and how it has been considered useful in empirical research. We describe how the demands of novel applications have stimulated methodological, computational, and inferential advancements.

Keyword(s): endogenous effects, longitudinal networks, point processes, relational events, social network analysis, time-stamped interaction data
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2024-04-22
2024-05-11
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Relational Event Modeling
Annual Review of Statistics and Its Application 11, 297 (2024); https://doi.org/10.1146/annurev-statistics-040722-060248
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/content/journals/10.1146/annurev-statistics-040722-060248
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  • Article Type: Review Article

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