Autonomous inference of complex network dynamics from incomplete and noisy data

TL;DR

This paper introduces a robust two-phase computational method for autonomously inferring complex network dynamics from incomplete and noisy data, applicable across various real-world systems including disease spread and social networks.

Contribution

The paper presents a novel two-phase approach that effectively infers complex network dynamics from imperfect data, demonstrating robustness and broad applicability.

Findings

Successfully inferred neuronal, genetic, social, and oscillator dynamics.

Captured early spreading dynamics of H1N1, SARS, and COVID-19.

Method is robust to noise, missing links, and heterogeneity.

Abstract

The availability of empirical data that capture the structure and behavior of complex networked systems has been greatly increased in recent years, however a versatile computational toolbox for unveiling a complex system's nodal and interaction dynamics from data remains elusive. Here we develop a two-phase approach for autonomous inference of complex network dynamics, and its effectiveness is demonstrated by the tests of inferring neuronal, genetic, social, and coupled oscillators dynamics on various synthetic and real networks. Importantly, the approach is robust to incompleteness and noises, including low resolution, observational and dynamical noises, missing and spurious links, and dynamical heterogeneity. We apply the two-phase approach to inferring the early spreading dynamics of H1N1 flu upon the worldwide airline network, and the inferred dynamical equation can also capture the spread of SARS and COVID-19 diseases. These findings together offer an avenue to discover the hidden microscopic mechanisms of a broad array of real networked systems.