Relational Event Modeling

TL;DR

The paper reviews the development and significance of Relational Event Models (REMs) for analyzing time-stamped interaction data, highlighting their methodological evolution and empirical applications.

Contribution

It provides a comprehensive overview of REMs, detailing their core properties, historical development, and how they have advanced empirical research in analyzing continuous-time relational data.

Findings

REMs effectively model continuous-time interaction data.

Methodological and computational improvements have enhanced REMs.

REMs have broad applications in social media and communication analysis.

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 led to further methodological improvements stimulated by new questions that REMs made possible. 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.