Modeling temporal networks using random itineraries

TL;DR

This paper introduces a method to generate dynamic temporal networks using random walks, capturing bursty and correlated behaviors, and demonstrates its effectiveness with a transportation system case study.

Contribution

It presents a novel procedure for creating synthetic temporal networks with realistic bursty and correlated dynamics using random itineraries.

Findings

Synthetic networks mimic empirical bursty behaviors

Method captures correlated temporal dynamics

Applicable to transportation and similar systems

Abstract

We propose a procedure to generate dynamical networks with bursty, possibly repetitive and correlated temporal behaviors. Regarding any weighted directed graph as being composed of the accumulation of paths between its nodes, our construction uses random walks of variable length to produce time-extended structures with adjustable features. The procedure is first described in a general framework. It is then illustrated in a case study inspired by a transportation system for which the resulting synthetic network is shown to accurately mimic the empirical phenomenology.