Relational event models with global covariates

TL;DR

This paper introduces a novel sampling method for relational event models that efficiently incorporates global covariates, demonstrated through bike sharing data analysis revealing weather and time effects.

Contribution

It proposes an innovative sampling approach for global covariates in relational event models, enabling efficient full likelihood estimation with large datasets.

Findings

Weather significantly affects bike sharing dynamics.

Time of day influences bike ride patterns.

Global covariates improve model insights.

Abstract

Bike sharing is an increasingly popular mobility choice as it is a sustainable, healthy and economically viable transportation mode. By interpreting rides between bike stations over time as temporal events connecting two bike stations, relational event models can provide important insights into this phenomenon. The focus of relational event models, as a typical event history model, is normally on dyadic or node-specific covariates, as global covariates are considered nuisance parameters in a partial likelihood approach. As full likelihood approaches are infeasible given the sheer size of the relational process, we propose an innovative sampling approach of temporally shifted non-events to recover important global drivers of the relational process. The method combines nested case-control sampling on a time-shifted version of the event process. This leads to a partial likelihood of the relational event process that is identical to that of a degenerate logistic additive model, enabling efficient estimation of both global and non-global covariate effects. The computational effectiveness of the method is demonstrated through a simulation study. The analysis of around 350,000 bike rides in the Washington D.C. area reveals significant influences of weather and time of day on bike sharing dynamics, besides a number of traditional node-specific and dyadic covariates.