Identification of complex networks from limited and noise contaminated data is an important yet challenging task, which has attracted researchers from different disciplines recently. In this paper, the underlying feature of a complex network identification problem was analyzed and translated into a sparse linear programming problem. Then, a general framework based on the Bayesian model with independent Laplace prior was proposed to guarantee the sparseness and accuracy of identification results after analyzing influences of different prior distributions. At the same time, a three-stage hierarchical method was designed to resolve the puzzle that the Laplace distribution is not conjugated to the normal distribution. Last, the variational Bayesian was introduced to improve the efficiency of the network reconstruction task. The high accuracy and robust properties of the proposed method were verified by conducting both general synthetic network and real network identification tasks based on the evolutionary game dynamic. Compared with other five classical algorithms, the numerical experiments indicate that the proposed model can outperform these methods in both accuracy and robustness.
Complex networks identification using Bayesian model with independent Laplace prior
Note: This paper belongs to the Focus Issue, Recent Advances in Modeling Complex Systems: Theory and Applications.
Yichi Zhang, Yonggang Li, Wenfeng Deng, Keke Huang, Chunhua Yang; Complex networks identification using Bayesian model with independent Laplace prior. Chaos 1 January 2021; 31 (1): 013107. https://doi.org/10.1063/5.0031134
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