# The Temporal Event Graph

## TL;DR

The paper introduces the temporal event graph (TEG), a lossless, static representation of temporal networks that captures inter-event times and motifs, enabling detailed analysis of dynamic interactions.

## Contribution

It presents the TEG as a novel, unique, and lossless method to analyze temporal networks without aggregation, capturing temporal motifs and inter-event times.

## Key findings

- TEG provides a natural decomposition of temporal networks.
- TEG effectively characterizes individual and collective behaviors.
- Demonstrated utility on synthetic and real networks.

## Abstract

Temporal networks are increasingly being used to model the interactions of complex systems. Most studies require the temporal aggregation of edges (or events) into discrete time steps to perform analysis. In this article we describe a static, lossless, and unique representation of a temporal network, the temporal event graph (TEG). The TEG describes the temporal network in terms of both the inter-event time and two-event temporal motif distributions. By considering these distributions in unison we provide a new method to characterise the behaviour of individuals and collectives in temporal networks as well as providing a natural decomposition of the network. We illustrate the utility of the TEG by providing examples on both synthetic and real temporal networks.