Analysing Collective Behaviour in Temporal Networks Using Event Graphs and Temporal Motifs

TL;DR

This paper introduces a novel method for analyzing collective behavior in temporal networks by decomposing them into macroscale components using temporal motifs and inter-event times, capturing both topological and temporal dynamics without aggregation.

Contribution

The paper presents a new approach to analyze collective behavior in temporal networks by combining topological and temporal information through motifs and inter-event times.

Findings

Effective decomposition of temporal networks into macroscale components.

Embedding of networks to describe behavior over multiple timescales.

Application demonstrated on online social network communication data.

Abstract

Historically studies of behaviour on networks have focused on the behaviour of individuals (node-based) or on the aggregate behaviour of the entire network. We propose a new method to decompose a temporal network into macroscale components and to analyse the behaviour of these components, or collectives of nodes, across time. This method utilises all available information in the temporal network (i.e. no temporal aggregation), combining both topological and temporal structure using temporal motifs and inter-event times. This allows us create an embedding of a temporal network in order to describe behaviour over time and at different timescales. We illustrate this method using an example of digital communication data collected from an online social network.