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 language | English (US) |
|---|---|
| Article number | 032317 |
| Journal | Physical Review E |
| Volume | 97 |
| Issue number | 3 |
| DOIs | |
| State | Published - Mar 28 2018 |
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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 journal › Article
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TY - JOUR
T1 - Sparse dynamical Boltzmann machine for reconstructing complex networks with binary dynamics
AU - Chen, Yu Zhong
AU - Lai, Ying-Cheng
PY - 2018/3/28
Y1 - 2018/3/28
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85044949706&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85044949706&partnerID=8YFLogxK
U2 - 10.1103/PhysRevE.97.032317
DO - 10.1103/PhysRevE.97.032317
M3 - Article
AN - SCOPUS:85044949706
VL - 97
JO - Physical Review E - Statistical, Nonlinear, and Soft Matter Physics
JF - Physical Review E - Statistical, Nonlinear, and Soft Matter Physics
SN - 1539-3755
IS - 3
M1 - 032317
ER -