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Identifying the diffusion source in complex networks with limited observers

Author

Listed:
  • Xu, Shuaishuai
  • Teng, Cong
  • Zhou, Yinzuo
  • Peng, Junhao
  • Zhang, Yicheng
  • Zhang, Zi-Ke

Abstract

Identifying sources of epidemic spreading or rumor diffusion from minority data is of paramount importance in network science with great applied values to the society. However, a general theoretical frame work dealing with source(s) localization is lacking of perfect understanding. Based on limited observers in the network, we study the problem of estimating the origin of a disease/rumor outbreak: given a contact network and a snapshot of epidemic spread at a certain time, root out the infection source. Assuming that the epidemic spread follows the usual susceptible–infected (SI) model, we introduce an inference algorithm based on sparsely placed observers. We present an algorithm which utilizes the correlated information between the network structure (shortest paths) and the diffusion dynamics (time sequence of infection). The numerical results of artificial and empirical networks show that it leads to significant improvement of performance compared to existing approaches. Our analysis sheds insight into the behavior of the disease/rumor spreading process not only in the local particular regime but also for the whole general network.

Suggested Citation

  • Xu, Shuaishuai & Teng, Cong & Zhou, Yinzuo & Peng, Junhao & Zhang, Yicheng & Zhang, Zi-Ke, 2019. "Identifying the diffusion source in complex networks with limited observers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
    • Handle: RePEc:eee:phsmap:v:527:y:2019:i:c:s0378437119307332
    DOI: 10.1016/j.physa.2019.121267
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    Citations

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    Cited by:

    1. Liu, Jiawei & Ding, Jie, 2020. "Requesting for retweeting or donating? A research on how the fundraiser seeks help in the social charitable crowdfunding," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
    2. 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).
    3. 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).
    4. Paluch, Robert & Gajewski, Łukasz G. & Suchecki, Krzysztof & Hołyst, Janusz A., 2021. "Impact of interactions between layers on source localization in multilayer networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 582(C).

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