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 monotonic 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.

Original languageEnglish (US)
Article number032303
JournalPhysical Review E - Statistical, Nonlinear, and Soft Matter Physics
Volume95
Issue number3
DOIs
StatePublished - Mar 2 2017

Fingerprint

Complex Networks
Binary
Linearization
linearization
complex systems
Signal Reconstruction
Monotonic Function
Binary Data
Stochastic Dynamics
Convex Optimization
Missing Data
Data-driven
Network Structure
binary data
Convert
Paradigm
Distinct
Predict
Demonstrate
Framework

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Statistics and Probability
  • Condensed Matter Physics

Cite this

Universal data-based method for reconstructing complex networks with binary-state dynamics. / Li, Jingwen; Shen, Zhesi; Wang, Wen Xu; Grebogi, Celso; Lai, Ying-Cheng.

In: Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, Vol. 95, No. 3, 032303, 02.03.2017.

Research output: Contribution to journalArticle

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