Data based reconstruction of complex multiplex networks

TL;DR

This paper introduces a mean-field maximum likelihood framework for reconstructing complex multiplex networks from data, addressing a key challenge in understanding layered systems with multiple types of interactions.

Contribution

It develops a novel data-driven reconstruction method for multiplex networks and analyzes its performance under various conditions, including noise and structural parameters.

Findings

Effective reconstruction of duplex networks demonstrated

Performance depends on structural and dynamical parameters

Framework validated on synthetic and real-world data

Abstract

It has been recognized that many complex dynamical systems in the real world require a description in terms of multiplex networks, where a set of common, mutually connected nodes belong to distinct network layers and play a different role in each layer. In spite of recent progress towards data based inference of single-layer networks, to reconstruct complex systems with a multiplex structure remains largely open. We articulate a mean-field based maximum likelihood estimation framework to solve this outstanding and challenging problem. We demonstrate the power of the reconstruction framework and characterize its performance using binary time series from a class of prototypical duplex network systems that host two distinct types of spreading dynamics. In addition to validating the framework using synthetic and real-world multiplex networks, we carry out a detailed analysis to elucidate the impacts of structural and dynamical parameters as well as noise on the reconstruction accuracy and robustness.