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Efficient network intervention with sampling information

Author

Listed:
  • Qi, Mingze
  • Tan, Suoyi
  • Chen, Peng
  • Duan, Xiaojun
  • Lu, Xin

Abstract

Most existing studies assume that the network topology is already known when designing intervention strategies, which is difficult to achieve in practice. This paper focuses on network intervention with sampling information and assumes that the nodes are obtained by three typical graph sampling algorithms. The characteristics of sampling nodes’ degrees and its influence on the design of intervention strategies are analyzed. Moreover, we propose a cutoff degree-based method for utilizing sampling information. Experiments in synthetic and real networks show that our method could effectively disintegrate networks by estimating networks’ mean degrees with sampling information. The results depend on the degree preference of sampling algorithms and the accuracy of the average degree estimation. For sampling algorithms with high degree preference, the intervention effect of sampling partial data could approach that of complete data when selecting the appropriate cutoff degree value.

Suggested Citation

  • Qi, Mingze & Tan, Suoyi & Chen, Peng & Duan, Xiaojun & Lu, Xin, 2023. "Efficient network intervention with sampling information," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    • Handle: RePEc:eee:chsofr:v:166:y:2023:i:c:s0960077922011316
    DOI: 10.1016/j.chaos.2022.112952
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    References listed on IDEAS

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    1. Joseph B. Bak-Coleman & Ian Kennedy & Morgan Wack & Andrew Beers & Joseph S. Schafer & Emma S. Spiro & Kate Starbird & Jevin D. West, 2022. "Combining interventions to reduce the spread of viral misinformation," Nature Human Behaviour, Nature, vol. 6(10), pages 1372-1380, October.
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    Cited by:

    1. Shen, Xiaoda & Wang, Zhigang & Deng, Ye & Wu, Jun, 2024. "Spatial network disintegration with heterogeneous cost," Chaos, Solitons & Fractals, Elsevier, vol. 187(C).
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