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

Jingwen Li, Zhesi Shen, Wen-Xu Wang, Celso Grebogi, Ying-Cheng Lai

Research output: Contribution to journalArticle

12 Citations (Scopus)
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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
Article number032303
Pages (from-to)1-12
Number of pages12
JournalPhysical Review. E, Statistical, Nonlinear and Soft Matter Physics
Volume95
Issue number3
DOIs
Publication statusPublished - 2 Mar 2017

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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

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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, p. 1-12.

Research output: Contribution to journalArticle

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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.",
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note = "ACKNOWLEDGMENTS W.-X.W. was supported by NSFC under Grant No. 61573064, No. 61074116, and No. 71631002, as well as the Fundamental Research Funds for the Central Universities, Beijing Nova Programme. Y.-C.L. was supported by ARO under Grant No. W911NF-14-1-0504. W.-X.W. designed research; J.L. and Z.S. performed research; all analyzed data; J.L., W.-X.W., and Y.-C.L. wrote the paper; all edited the paper. The authors declare no competing financial interests.",
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