# Reliability of relational event model estimates under sampling: how to fit a relational event model to 360 million dyadic events

## TL;DR

This paper evaluates the reliability of relational event model estimates under different sampling schemes, demonstrating that large networks can be effectively analyzed with relatively small samples, and providing practical guidance for empirical studies.

## Contribution

It introduces a systematic assessment of sampling schemes for relational event models and offers open-source tools for reliable model fitting on large-scale networks.

## Key findings

- Relational event models can be reliably fitted to networks with over 12 million nodes and 360 million dyadic events using small samples.
- Sampling scheme choice significantly affects the estimation of network effects.
- Open-source software facilitates flexible and reliable analysis of large relational event data.

## Abstract

We assess the reliability of relational event model parameters estimated under two sampling schemes: (1) uniform sampling from the observed events and (2) case-control sampling which samples non-events, or null dyads ("controls"), from a suitably defined risk set. We experimentally determine the variability of estimated parameters as a function of the number of sampled events and controls per event, respectively. Results suggest that relational event models can be reliably fitted to networks with more than 12 million nodes connected by more than 360 million dyadic events by analyzing a sample of some tens of thousands of events and a small number of controls per event. Using data that we collected on the Wikipedia editing network, we illustrate how network effects commonly included in empirical studies based on relational event models need widely different sample sizes to be estimated reliably. For our analysis we use an open-source software which implements the two sampling schemes, allowing analysts to fit and analyze relational event models to the same or other data that may be collected in different empirical settings, varying sample parameters or model specification.