Bayesian inference of network structure from unreliable data

TL;DR

This paper introduces a Bayesian approach for reconstructing complex network structures from unreliable, error-prone observational data, enabling more accurate network analysis despite measurement uncertainties.

Contribution

It presents a fully Bayesian method for network reconstruction that handles unknown and substantial measurement errors, with practical implementation and real-world case studies.

Findings

Effective reconstruction from error-prone data

Applicable to various data formats and error types

Computationally efficient and publicly available code

Abstract

Most empirical studies of complex networks do not return direct, error-free measurements of network structure. Instead, they typically rely on indirect measurements that are often error-prone and unreliable. A fundamental problem in empirical network science is how to make the best possible estimates of network structure given such unreliable data. In this paper we describe a fully Bayesian method for reconstructing networks from observational data in any format, even when the data contain substantial measurement error and when the nature and magnitude of that error is unknown. The method is introduced through pedagogical case studies using real-world example networks, and specifically tailored to allow straightforward, computationally efficient implementation with a minimum of technical input. Computer code implementing the method is publicly available.