Modelling Interaction Duration in Relational Event Models

2026-02-24

AI Summary

This study addresses a key limitation of traditional relational event models, which neglect event duration and thus struggle to capture its influence on subsequent interactions. To overcome this, we propose the Duration-based Relational Event Model (DuREM), which explicitly incorporates event duration into the modeling framework for the first time. DuREM jointly models the occurrence of new events and the termination of ongoing ones by extending the risk set, and introduces historical duration as an endogenous statistic. Implemented in R via the durem package, the model integrates covariates with dynamic duration effects, substantially enhancing the representation of interaction processes. Empirical applications to team dynamics and interpersonal violence demonstrate that DuREM effectively uncovers the critical role of event duration in shaping subsequent interaction patterns.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Knowledge Representation and Reasoning: Action, Change, and Causality
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems
• Web Mining and Content Analysis: Models for Web evolution

Abstract

The study of relational events, which are interactions occurring between actors over time, has gained significant traction recently. Traditional relational event models typically focus on modelling the occurrence and sequence of events without considering their duration even though duration information is frequently available in empirical relational event data. We introduce a novel Duration Relational Event Model (DuREM) that incorporates the temporal duration of events into the analysis. The proposed model extends the existing framework by (i) allowing the inclusion of past event durations in the endogenous statistics to account for how the duration of past events affects the rate of future interactions, and (ii) extending the traditional relational event model by also modelling when events will end based on past event history and covariates. This is achieved by extending the risk set to include both ongoing events at risk of ending and idle dyads at risk of starting new events. The methodology is implemented in a new R package `durem'. Two case studies concerning team dynamics and inter-personal violence are presented to illustrate the applicability of the model.

Problem

• relational event models
• interaction duration
• temporal dynamics
• event history
• duration modeling

Innovation

• relational event model
• event duration
• temporal dynamics
• risk set
• DuREM