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The reconstruction on the game networks with binary-state and multi-state dynamics

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  • Junfang Wang
  • Jin-Li Guo

Abstract

Reconstruction of network is to infer the relationship among nodes using observation data, which is helpful to reveal properties and functions of complex systems. In view of the low reconstruction accuracy based on small data and the subjectivity of threshold to infer adjacency matrix, the paper proposes two models: the quadratic compressive sensing (QCS) and integer compressive sensing (ICS). Then a combined method (CCS) is given based on QCS and ICS, which can be used on binary-state and multi-state dynamics. It is found that CCS is usually a superior method comparing with compressive sensing, LASSO on several networks with different structures and scales. And it can infer larger node correctly than the other two methods. The paper is conducive to reveal the hidden relationship with small data so that to understand, predicate and control a vast intricate system.

Suggested Citation

  • Junfang Wang & Jin-Li Guo, 2022. "The reconstruction on the game networks with binary-state and multi-state dynamics," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-18, February.
    • Handle: RePEc:plo:pone00:0263939
    DOI: 10.1371/journal.pone.0263939
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    References listed on IDEAS

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    1. Shuixia Guo & Jianhua Wu & Mingzhou Ding & Jianfeng Feng, 2008. "Uncovering Interactions in the Frequency Domain," PLOS Computational Biology, Public Library of Science, vol. 4(5), pages 1-10, May.
    2. 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.
    3. Imhof, Lorens & Nowak, Martin & Fudenberg, Drew, 2007. "Tit-for-Tat or Win-Stay, Lose-Shift?," Scholarly Articles 3200671, Harvard University Department of Economics.
    4. 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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