Introduction to using relational hyperevent models to study criminal networks
Bibliographic Data
| ID | 21785702 |
|---|---|
| Authors | Tomáš Diviák (0000-0001-7239-8466, University of Manchester, corresponding author), Jürgen Lerner (University of Konstanz) |
| Year | 2026 |
| Pages | 1-23 |
| Publication date | 2026-06-30 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Global Crime (JOURNAL) |
| Journal identifiers | ISSN: 1744-0572 • E-ISSN: 1744-0580 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/17440572.2026.2689945 |
| OpenAlex | W7166831141 |
| Language | EN |
| References cited | 40 |
Relational hyperevent data capture time-stamped/-ordered interactions among any number of nodes in a network. Such data are central in criminal network research, both in studies on social organisation of crime and on co-offending. Until recently, researchers had to coerce these data for standard network methods, risking loss of information and biased results. The relational hyperevent model (RHEM) solves this by modelling hyperevent data directly, preserving their temporal and structural detail.This article introduces RHEM to criminal network researchers. We show how to collect and prepare data for RHEM, outline the model’s logic in non-technical terms, compare it to other models and highlight the theoretical questions RHEM enables to answer. Two examples – a directed communication network and an undirected co-offending network – were used to illustrate the full workflow from data processing to model interpretation. Finally, we point the readers to RHEM applications in both criminal and non-criminal network studies for inspiration
Network model · Network science · Relational database · Relational model · Social network analysis · Workflow · Complex Network Analysis Techniques · Crime Patterns and Interventions · Crime, Illicit Activities, and Governance
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Exponential Random Graph Models for Social Networks
Illicit Network Dynamics
Regression Models and Life-Tables
Investigating the Dynamics of Outlaw Motorcycle Gang Co-Offending Networks
Understanding the Mechanisms that Drive Relational Events Dynamics and Structure in Corruption Networks
Inside Criminal Networks
The network dynamics of co-offending careers
Overlapping crime
Using social network analysis to study crime
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Introduction to stochastic actor-based models for network dynamics
Offence versatility among co-offenders
Criminal collaboration and risk
Uncloaking Terrorist Networks
Come at the king, you best not miss
Law-Enforcement Disruption of a Drug Importation Network
Criminal Achievement, Offender Networks and the Benefits of Low Self‐control
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Key aspects of covert networks data collection
Theories of Communication Networks
The tangled history of social network analysis and gang research—A long way from Street Corner Society
Co-offending among outlaw motorcycle gang members
Dynamic Network Actor Models
A Relational Event Framework for Social Action
Shining a Light on the Shadows
Dynamics and disruption
| Citation velocity | historical |
|---|---|
| Highly cited | No |