Sparse dynamical Boltzmann machine for reconstructing complex networks with binary dynamics

2016/11/06 by Yu-Zhong Chen, Ying‐Cheng Lai, Ying-Cheng Lai
Mathematics · Neuroscience · Physics and Astronomy · #Algorithm #Artificial intelligence #Artificial neural network #Binary number #Bioinformatics #Boltzmann machine #Cluster analysis #Complex network #Complex system #Computer science #DECIPHER #Dynamical systems theory #Estimator #Mathematics #Neural dynamics and brain function #Physics #Process (computing) #Random lasers and scattering media #Restricted Boltzmann machine #Statistical physics #Theoretical computer science #Topology (electrical circuits) #physics.data-an #physics.soc-ph #stochastic dynamics and bifurcation

paper · pdf · doi:10.1103/physreve.97.032317

published as Phys. Rev. E 97, 032317 (2018)

arxiv created 2016/11/06 · openalex publication_date 2018/03/28 · arxiv updated 2018/04/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

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.

Citations

• On Information and Sufficiency
• Robust Reconstruction of Complex Networks from Sparse Data
• Food-web structure and network theory: The role of connectance and size
• Evolution of sociology freshmen into a friendship network
• Evolutionary prisoner’s dilemma game on a square lattice
• Epidemic Spreading in Scale-Free Networks
• Statistical physics of social dynamics
• The Evolution of Cooperation
• Self-Organized Networks of Competing Boolean Agents
• Mean-Field Theory for the Inverse Ising Problem at Low Temperatures
• Compressive Sensing [Lecture Notes]
• An Introduction To Compressive Sampling [A sensing/sampling paradigm that goes against the common knowledge in data acquisition]
• Stable signal recovery from incomplete and inaccurate measurements
• Discovering governing equations from data: Sparse identification of nonlinear dynamical systems
• Compressed sensing