Simulating Relational Event Histories: Why and How

2024-03-28

AI Summary

Addressing core challenges in relational event network modeling—including difficult model evaluation, weak theoretical foundations, lack of principled intervention design, and low predictive interpretability—this paper introduces the first statistical simulation framework specifically designed for relational event networks. It integrates binary dependence structures with actor-oriented mechanisms to enable fine-grained, temporally resolved Monte Carlo generation of relational events. Methodologically, it is the first work to systematically unify relational event simulation with social network analysis across four key application scenarios: model diagnostics, theory testing, intervention forecasting, and structural attribution. We release an open-source R package, *remulate*, ensuring reproducibility and interpretability. Through three empirical case studies—criminal gang evolution, validation of optimal distinctiveness theory, and network intervention analysis—we demonstrate the framework’s effectiveness in enhancing model discriminability, advancing theoretical development, and quantifying causal intervention effects.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior

Application Category

• Web Mining and Content Analysis: Web data generation and simulation
• Social Networks and Social Media: Computational social science
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs

Abstract

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.

Problem

• Social Network Analysis
• Statistical Simulation
• Interpersonal Interaction

Innovation

• Social Network Analysis
• Statistical Methodology
• Network Intervention