Abstract

Revealing the structure and dynamics of complex networked systems from observed data is a problem of current interest. Is it possible to develop a completely data-driven framework to decipher the network structure and different types of dynamical processes on complex networks? We develop a model named sparse dynamical Boltzmann machine (SDBM) as a structural estimator for complex networks that host binary dynamical processes. The SDBM attains its topology according to that of the original system and is capable of simulating the original binary dynamical process. We develop a fully automated method based on compressive sensing and a clustering algorithm to construct the SDBM. We demonstrate, for a variety of representative dynamical processes on model and real world complex networks, that the equivalent SDBM can recover the network structure of the original system and simulates its dynamical behavior with high precision.

Original languageEnglish (US)
Article number032317
JournalPhysical Review E
Volume97
Issue number3
DOIs
StatePublished - Mar 28 2018

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Boltzmann Machine
Complex Networks
Binary
Network Structure
Compressive Sensing
Data-driven
Dynamical Behavior
Clustering Algorithm
complex systems
estimators
Topology
Estimator
topology
Model
Demonstrate

ASJC Scopus subject areas

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

Cite this

Sparse dynamical Boltzmann machine for reconstructing complex networks with binary dynamics. / Chen, Yu Zhong; Lai, Ying-Cheng.

In: Physical Review E, Vol. 97, No. 3, 032317, 28.03.2018.

Research output: Contribution to journalArticle

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