A universal data based method for reconstructing complex networks with binary-state dynamics

TL;DR

This paper introduces a universal, data-driven linearization method for reconstructing complex networks with binary-state dynamics, applicable to diverse systems with high accuracy even with noisy or incomplete data.

Contribution

It develops a general linearization framework that transforms network reconstruction into a sparse signal problem solvable by convex optimization, without prior knowledge of dynamics.

Findings

High reconstruction accuracy across various network types

Robust performance with noisy and missing data

Applicable to systems with linear, nonlinear, and stochastic dynamics

Abstract

To understand, predict, and control complex networked systems, a prerequisite is to reconstruct the network structure from observable data. Despite recent progress in network reconstruction, binary-state dynamics that are ubiquitous in nature, technology and society still present an outstanding challenge in this field. Here we offer a framework for reconstructing complex networks with binary-state dynamics by developing a universal data-based linearization approach that is applicable to systems with linear, nonlinear, discontinuous, or stochastic dynamics governed by monotonous functions. The linearization procedure enables us to convert the network reconstruction into a sparse signal reconstruction problem that can be resolved through convex optimization. We demonstrate generally high reconstruction accuracy for a number of complex networks associated with distinct binary-state dynamics from using binary data contaminated by noise and missing data. Our framework is completely data driven, efficient and robust, and does not require any a priori knowledge about the detailed dynamical process on the network. The framework represents a general paradigm for reconstructing, understanding, and exploiting complex networked systems with binary-state dynamics.