Introduction to Relational Event Modelling

TL;DR

This paper provides a practical tutorial on relational event models (REMs), explaining their theory, simulation, and empirical applications to help researchers analyze interaction data over time.

Contribution

It offers the first comprehensive hands-on tutorial on REMs, integrating recent advances, simulation techniques, and empirical case studies.

Findings

Demonstrates how to simulate synthetic relational-event data.

Compares different modelling and inference strategies.

Provides practical guidance for applying REMs to real data.

Abstract

Interactions and time shape many aspects of life. Everyday activities -- like conversations, emails, money transfers, citations, and even acts of violence -- are relational events: interactions between a sender and a receiver at a specific moment. At the intersection of event-history analysis and network modelling, relational event models (REMs) offer a powerful framework for studying when and why these events occur. Recent advances have made it possible to express REMs as generalized additive models, allowing researchers to capture complex, non-linear patterns over time. While an essay and a comprehensive review exist, a hands-on tutorial paper on REMs is still missing. This work fills that gap. It provides a practical introduction to REMs, incorporating the latest developments in the field. It demonstrates how to simulate synthetic relational-event data and walks through several empirical applications, comparing different modelling and inference strategies. By bringing together theory, simulation, and application, this tutorial lowers the barrier to entry and makes REMs a more accessible and practical tool.