Sparse Bayesian learning for network structure reconstruction based on evolutionary game data

Keke Huang, Wenfeng Deng, Yichi Zhang and Hongqiu Zhu

Physica A: Statistical Mechanics and its Applications, 2020, vol. 541, issue C

Abstract: Network structure reconstruction is a fundamental problem for understanding, predicting and controlling the behaviors of complex networked systems and has received growing attention due to the potentials in a wide range of fields. Recent years have witnessed dramatic advances in the field of network structure reconstruction, especially the famous compressed sensing-based methods. However, some neglected disadvantages still exist in the existing works, such as the high measurement correlation existing in the solution matrix, reconstruction behaviors subject to model-based constraints and pure point estimate of the reconstruction results without credibility, which inevitably drag down the reconstruction performance. To address these problems, we propose a new framework of sparse Bayesian learning for network structure reconstruction based on evolutionary game data from the perspective of Bayesian and statistics. Specifically, we formulate the problem of network structure reconstruction as a Bayesian compressed sensing problem. Then, a hierarchical prior model is invoked for conjugated Bayesian inference to obtain the posterior distribution of the reconstructed result, including the reconstructed mean and covariance. Finally, the parameters in the reconstructed results are updated by an iterative estimation procedure. Results from numerical experiments have demonstrated applicability and efficiency of the proposed method and presented superiority over other reconstruction methods.

Keywords: Network structure reconstruction; Evolutionary game; Compressed sensing; Sparse Bayesian learning (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437119320102
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:541:y:2020:i:c:s0378437119320102

DOI: 10.1016/j.physa.2019.123605

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().