arXiv subjects

Roger Leenders

Publications and source records attributed to Roger Leenders.

4 recordsLinked to original sources

arXiv2026

Modelling Interaction Duration in Relational Event Models

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.

cs.SI↗

arXiv2025

Simulating Relational Event Histories: Why and How

Many important social phenomena are characterized by repeated interactions among individuals over time such as email exchanges in an organization or face-to-face interactions in a classroom. To understand the underlying mechanisms of social interaction dynamics, statistical simulation techniques of longitudinal network data on a fine temporal granularity are crucially important. This paper makes two contributions to the field. First, we present statistical frameworks to simulate relational event networks under dyadic and actor-oriented relational event models which are implemented in a new R package 'remulate'. Second, we explain how the simulation framework can be used to address challenging problems in temporal social network analysis, such as model fit assessment, theory building, network intervention planning, making predictions, understanding the impact of network structures, to name a few. This is shown in three extensive case studies. In the first study, it is elaborated why simulation-based techniques are crucial for relational event model assessment which is illustrated for a network of criminal gangs. In the second study, it is shown how simulation techniques are important when building and extending theories about social phenomena which is illustrated via optimal distinctiveness theory. In the third study, we demonstrate how simulation techniques contribute to a better understanding of the longevity and the potential effect sizes of network interventions. Through these case studies and software, researchers will be able to better understand social interaction dynamics using relational event data from real-life networks.

cs.SI↗

arXiv2025

Not All Bonds Are Created Equal: Dyadic Latent Class Models for Relational Event Data

Dynamic social networks can be conceptualized as sequences of dyadic interactions between individuals over time. The relational event model has been the workhorse to analyze such interaction sequences in empirical social network research. When addressing possible unobserved heterogeneity in the interaction mechanisms, standard approaches, such as the stochastic block model, aim to cluster the variation at the actor level. Though useful, the implied latent structure of the adjacency matrix is restrictive which may lead to biased interpretations and insights. To address this shortcoming, we introduce a more flexible dyadic latent class relational event model (DLC-REM) that captures the unobserved heterogeneity at the dyadic level. Through numerical simulations, we provide a proof of concept demonstrating that this approach is more general than latent actor-level approaches. To illustrate the applicability of the model, we apply it to a dataset of militarized interstate conflicts between countries.

cs.SI↗

arXiv2023

A Bayesian actor-oriented multilevel relational event model with hypothesis testing procedures

Relational event network data are becoming increasingly available. Consequently, statistical models for such data have also surfaced. These models mainly focus on the analysis of single networks, while in many applications, multiple independent event sequences are observed, which are likely to display similar social interaction dynamics. Furthermore, statistical methods for testing hypotheses about social interaction behavior are underdeveloped. Therefore, the contribution of the current paper is twofold. First, we present a multilevel extension of the dynamic actor-oriented model, which allows researchers to model sender and receiver processes separately. The multilevel formulation enables principled probabilistic borrowing of information across networks to accurately estimate drivers of social dynamics. Second, a flexible methodology is proposed to test hypotheses about common and heterogeneous social interaction drivers across relational event sequences. Social interaction data between children and teachers in classrooms are used to showcase the methodology.

stat.ME↗