Building surrogate temporal network data from observed backbones

TL;DR

This paper proposes systematic methods to create surrogate temporal network data from backbone extraction, enabling effective data-driven simulations while retaining essential information from original networks.

Contribution

It introduces procedures for building surrogate data from temporal network backbones, addressing how to preserve information for simulations.

Findings

Surrogate data can effectively replicate original network dynamics.

Retention of key information enables accurate spreading process simulations.

Methods are validated on diverse empirical temporal networks.

Abstract

In many data sets, crucial elements co-exist with non-essential ones and noise. For data represented as networks in particular, several methods have been proposed to extract a "network backbone", i.e., the set of most important links. However, the question of how the resulting compressed views of the data can effectively be used has not been tackled. Here we address this issue by putting forward and exploring several systematic procedures to build surrogate data from various kinds of temporal network backbones. In particular, we explore how much information about the original data need to be retained alongside the backbone so that the surrogate data can be used in data-driven numerical simulations of spreading processes. We illustrate our results using empirical temporal networks with a broad variety of structures and properties.