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Identification of efficient observers for locating spreading source in complex networks

Author

Listed:
  • Zhang, Xizhe
  • Zhang, Yubo
  • Lv, Tianyang
  • Yin, Ying

Abstract

Estimating the location of the spreading source of complex networks is a challenging task and plays an important role in many real problems. The hidden source can be localized based on the information gathered by a few nodes, which are called the observers. Identification of the efficient observers is critical to locate the source with high accuracy. Here we analyze several placement strategies of the observers based on centralities of nodes, including the high-degree, high-betweenness, high-clustering coefficient, high-eigenvector and high-closeness. Based on the random spreading experiments on both model and real networks, we find that the localization accuracy of these strategies is decreased with the increase of the connectivity of network, and there is no significant difference between them. Further experiments show that the coverage range of the observers may be the key factor that affects the localization accuracy. Our results can provide a route for the optimal design of placement strategies of the observer nodes.

Suggested Citation

  • Zhang, Xizhe & Zhang, Yubo & Lv, Tianyang & Yin, Ying, 2016. "Identification of efficient observers for locating spreading source in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 442(C), pages 100-109.
    • Handle: RePEc:eee:phsmap:v:442:y:2016:i:c:p:100-109
    DOI: 10.1016/j.physa.2015.09.017
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    Citations

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    Cited by:

    1. Xu, Hao & Li, Tao & Liu, Xiongding & Liu, Wenjin & Dong, Jing, 2019. "Spreading dynamics of an online social rumor model with psychological factors on scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 234-246.
    2. Huo, Liang’an & Wang, Li & Zhao, Xiaomin, 2019. "Stability analysis and optimal control of a rumor spreading model with media report," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 517(C), pages 551-562.
    3. Huo, Liang’an & Wang, Li & Song, Guoxiang, 2017. "Global stability of a two-mediums rumor spreading model with media coverage," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 757-771.
    4. 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.
    5. Huo, Liang’an & Song, Naixiang, 2016. "Dynamical interplay between the dissemination of scientific knowledge and rumor spreading in emergency," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 73-84.
    6. 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.
    7. Huo, Liang'an & Wang, Li & Song, Naixiang & Ma, Chenyang & He, Bing, 2017. "Rumor spreading model considering the activity of spreaders in the homogeneous network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 855-865.

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