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Reconstructing propagation networks with natural diversity and identifying hidden sources

Author

Listed:
  • Zhesi Shen

    (School of Systems Science, Beijing Normal University)

  • Wen-Xu Wang

    (School of Systems Science, Beijing Normal University
    School of Electrical, Computer and Energy Engineering, Arizona State University)

  • Ying Fan

    (School of Systems Science, Beijing Normal University)

  • Zengru Di

    (School of Systems Science, Beijing Normal University)

  • Ying-Cheng Lai

    (School of Electrical, Computer and Energy Engineering, Arizona State University
    Arizona State University)

Abstract

Our ability to uncover complex network structure and dynamics from data is fundamental to understanding and controlling collective dynamics in complex systems. Despite recent progress in this area, reconstructing networks with stochastic dynamical processes from limited time series remains to be an outstanding problem. Here we develop a framework based on compressed sensing to reconstruct complex networks on which stochastic spreading dynamics take place. We apply the methodology to a large number of model and real networks, finding that a full reconstruction of inhomogeneous interactions can be achieved from small amounts of polarized (binary) data, a virtue of compressed sensing. Further, we demonstrate that a hidden source that triggers the spreading process but is externally inaccessible can be ascertained and located with high confidence in the absence of direct routes of propagation from it. Our approach thus establishes a paradigm for tracing and controlling epidemic invasion and information diffusion in complex networked systems.

Suggested Citation

  • 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.
  • Handle: RePEc:nat:natcom:v:5:y:2014:i:1:d:10.1038_ncomms5323
    DOI: 10.1038/ncomms5323
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    Citations

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    Cited by:

    1. 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.
    2. Huan Wang & Chuang Ma & Han-Shuang Chen & Ying-Cheng Lai & Hai-Feng Zhang, 2022. "Full reconstruction of simplicial complexes from binary contagion and Ising data," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    3. Xu, Hai-Chuan & Wang, Zhi-Yuan & Jawadi, Fredj & Zhou, Wei-Xing, 2023. "Reconstruction of international energy trade networks with given marginal data: A comparative analysis," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    4. Wu, Qingchu, 2024. "A hybrid one-vertex model for susceptible–infected–susceptible diseases on networks with partial connection information," Chaos, Solitons & Fractals, Elsevier, vol. 178(C).
    5. Wang, Hongjue, 2019. "An universal algorithm for source location in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 620-630.
    6. 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.
    7. Pandey, Pradumn Kumar & Badarla, Venkataramana, 2018. "Reconstruction of network topology using status-time-series data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 573-583.
    8. Ming-Yang Zhou & Hao Liao & Wen-Man Xiong & Xiang-Yang Wu & Zong-Wen Wei, 2017. "Connecting Patterns Inspire Link Prediction in Complex Networks," Complexity, Hindawi, vol. 2017, pages 1-12, December.
    9. Yanqiao Zheng & Xiaobing Zhao & Xiaoqi Zhang & Xinyue Ye & Qiwen Dai, 2019. "Mining the Hidden Link Structure from Distribution Flows for a Spatial Social Network," Complexity, Hindawi, vol. 2019, pages 1-17, May.
    10. Li, Ruiqi & Wang, Wenxu & Di, Zengru, 2017. "Effects of human dynamics on epidemic spreading in Côte d’Ivoire," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 467(C), pages 30-40.
    11. Hang, Zihua & Dai, Penglin & Jia, Shanshan & Yu, Zhaofei, 2020. "Network structure reconstruction with symmetry constraint," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    12. Huang, Qiangjuan & Zhao, Chengli & Zhang, Xue & Yi, Dongyun, 2017. "Locating the source of spreading in temporal networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 434-444.
    13. Leto Peel & Tiago P. Peixoto & Manlio De Domenico, 2022. "Statistical inference links data and theory in network science," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    14. Yin, Haofei & Zhang, Aobo & Zeng, An, 2023. "Identifying hidden target nodes for spreading in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    15. 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).

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