Stochastic network modeling as generative social science

Steglich, Christian; Snijders, Tom A. B.

doi:10.4337/9781789909432.00012

Cited by 3 publications

(1 citation statement)

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“…This opens the potential of marrying statistical analyses of network data with theoretical agent-based models (ABMs). Researchers from empirical network research and ABMs for networks have called for better empirically calibrated, theoretically meaningful models (Snijders & Steglich, 2015;Flache et al, 2017;Stadtfeld & Amati, 2021;Steglich & Snijders, 2022). Relational event models seem like a promising framework to achieve this, in particular as simulation engines are already available in REM software packages.…”

Section: Looking Forward: the Future Of Relational Event Modelsmentioning

confidence: 99%

Relational event models in network science

Butts

Lomi

Snijders

et al. 2023

Net Sci

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Relational event models (REMs) for the analysis of social interaction were first introduced 15 years ago. Since then, a number of important substantive and methodological contributions have produced their progressive refinement and hence facilitated their increased adoption in studies of social and other networks. Today REMs represent a well-established class of statistical models for relational processes. This special issue of Network Science demonstrates the standing and recognition that REMs have achieved within the network analysis and networks science communities. We wrote this brief introductory editorial essay with four main objectives in mind: (i) positioning relational event data and models in the larger context of contemporary network science and social network research; (ii) reviewing some of the most important recent developments; (iii) presenting the innovative studies collected in this special issue as evidence of the empirical value of REMs, and (iv) identifying open questions and future research directions.

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Section: Looking Forward: the Future Of Relational Event Modelsmentioning

confidence: 99%