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Source estimation in continuous-time diffusion networks via incomplete observation

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  • Shi, Chaoyi
  • Zhang, Qi
  • Chu, Tianguang

Abstract

This paper considers the problem of estimating the source of diffusion in a network under incomplete observation condition. The diffusion process is described by a continuous-time information diffusion model and the source estimation is formulated as a maximum likelihood (ML) estimator in terms of a Gaussian weighted averaging of the correlation coefficients between the observed activation times and the sampled transmission delays obtained by Monte Carlo simulations. Experiments are worked out with both synthetic and real-world networks to show the effectiveness of our method in comparison with previous results.

Suggested Citation

  • Shi, Chaoyi & Zhang, Qi & Chu, Tianguang, 2022. "Source estimation in continuous-time diffusion networks via incomplete observation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 592(C).
    • Handle: RePEc:eee:phsmap:v:592:y:2022:i:c:s0378437121009985
    DOI: 10.1016/j.physa.2021.126843
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    References listed on IDEAS

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    1. Gajewski, Ł.G. & Suchecki, K. & Hołyst, J.A., 2019. "Multiple propagation paths enhance locating the source of diffusion in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 34-41.
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    Cited by:

    1. Li, Ziqi & Shi, Chaoyi & Zhang, Qi & Chu, Tianguang, 2024. "Inferring the source of diffusion in networks under weak observation condition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).

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