Temporal Configuration Model: Statistical Inference and Spreading Processes

TL;DR

This paper introduces a family of temporal network models extending the configuration model, provides estimators for their parameters, and analyzes spreading processes, validated through simulations and empirical data.

Contribution

It presents a novel family of temporal configuration models with consistent parameter estimators and analytical solutions for epidemic spreading metrics.

Findings

Estimators perform well on finite samples.

Analytical solutions for reproductive numbers are derived.

Models effectively fit empirical proximity networks.

Abstract

We introduce a family of parsimonious network models that are intended to generalize the configuration model to temporal settings. We present consistent estimators for the model parameters and perform numerical simulations to illustrate the properties of the estimators on finite samples. We also develop analytical solutions for basic and effective reproductive numbers for the early stage of discrete-time SIR spreading process. We apply three distinct temporal configuration models to empirical student proximity networks and compare their performance.