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

Author

Listed:
  • Huang, Keke
  • Deng, Wenfeng
  • Zhang, Yichi
  • Zhu, Hongqiu

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.

Suggested Citation

  • Huang, Keke & Deng, Wenfeng & Zhang, Yichi & Zhu, Hongqiu, 2020. "Sparse Bayesian learning for network structure reconstruction based on evolutionary game data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
  • Handle: RePEc:eee:phsmap:v:541:y:2020:i:c:s0378437119320102
    DOI: 10.1016/j.physa.2019.123605
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    References listed on IDEAS

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    1. Zhesi Shen & Wen-Xu Wang & Ying Fan & Zengru Di & Ying-Cheng Lai, 2014. "Reconstructing propagation networks with natural diversity and identifying hidden sources," Nature Communications, Nature, vol. 5(1), pages 1-10, September.
    2. Long Ma & Xiao Han & Zhesi Shen & Wen-Xu Wang & Zengru Di, 2015. "Efficient Reconstruction of Heterogeneous Networks from Time Series via Compressed Sensing," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-12, November.
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    Citations

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    Cited by:

    1. Lin, Jingyan & Huang, Changwei & Dai, Qionglin & Yang, Junzhong, 2020. "Evolutionary game dynamics of combining the payoff-driven and conformity-driven update rules," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).

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