IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v166y2023ics0960077922011316.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077922011316
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2022.112952?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    2. Xin Lu & Linus Bengtsson & Tom Britton & Martin Camitz & Beom Jun Kim & Anna Thorson & Fredrik Liljeros, 2012. "The sensitivity of respondent‐driven sampling," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 175(1), pages 191-216, January.
    3. Flaviano Morone & Hernán A. Makse, 2015. "Correction: Corrigendum: Influence maximization in complex networks through optimal percolation," Nature, Nature, vol. 527(7579), pages 544-544, November.
    4. Samuel F Rosenblatt & Jeffrey A Smith & G Robin Gauthier & Laurent Hébert-Dufresne, 2020. "Immunization strategies in networks with missing data," PLOS Computational Biology, Public Library of Science, vol. 16(7), pages 1-21, July.
    5. Marco Grassia & Manlio De Domenico & Giuseppe Mangioni, 2021. "Machine learning dismantling and early-warning signals of disintegration in complex systems," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    6. Shang, Yilun, 2021. "Generalized k-cores of networks under attack with limited knowledge," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    7. Flaviano Morone & Hernán A. Makse, 2015. "Influence maximization in complex networks through optimal percolation," Nature, Nature, vol. 524(7563), pages 65-68, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wu, Rui-Jie & Kong, Yi-Xiu & Di, Zengru & Zhang, Yi-Cheng & Shi, Gui-Yuan, 2022. "Analytical solution to the k-core pruning process," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    2. Fink, Christian G. & Fullin, Kelly & Gutierrez, Guillermo & Omodt, Nathan & Zinnecker, Sydney & Sprint, Gina & McCulloch, Sean, 2023. "A centrality measure for quantifying spread on weighted, directed networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
    3. Gangwal, Utkarsh & Singh, Mayank & Pandey, Pradumn Kumar & Kamboj, Deepak & Chatterjee, Samrat & Bhatia, Udit, 2022. "Identifying early-warning indicators of onset of sudden collapse in networked infrastructure systems against sequential disruptions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 591(C).
    4. Wandelt, Sebastian & Sun, Xiaoqian & Zhang, Anming, 2023. "Towards analyzing the robustness of the Integrated Global Transportation Network Abstraction (IGTNA)," Transportation Research Part A: Policy and Practice, Elsevier, vol. 178(C).
    5. Li, Sheng & Liu, Wenwen & Wu, Ruizi & Li, Junli, 2023. "An adaptive attack model to network controllability," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    6. James Flamino & Alessandro Galeazzi & Stuart Feldman & Michael W. Macy & Brendan Cross & Zhenkun Zhou & Matteo Serafino & Alexandre Bovet & Hernán A. Makse & Boleslaw K. Szymanski, 2023. "Political polarization of news media and influencers on Twitter in the 2016 and 2020 US presidential elections," Nature Human Behaviour, Nature, vol. 7(6), pages 904-916, June.
    7. Li Zeng & Changjun Fan & Chao Chen, 2023. "Leveraging Minimum Nodes for Optimum Key Player Identification in Complex Networks: A Deep Reinforcement Learning Strategy with Structured Reward Shaping," Mathematics, MDPI, vol. 11(17), pages 1-13, August.
    8. Tianle Pu & Li Zeng & Chao Chen, 2024. "Deep Reinforcement Learning for Network Dismantling: A K-Core Based Approach," Mathematics, MDPI, vol. 12(8), pages 1-12, April.
    9. Zhang, Dayong & Men, Hao & Zhang, Zhaoxin, 2024. "Assessing the stability of collaboration networks: A structural cohesion analysis perspective," Journal of Informetrics, Elsevier, vol. 18(1).
    10. Sun, Peng Gang & Che, Wanping & Quan, Yining & Wang, Shuzhen & Miao, Qiguang, 2022. "Random networks are heterogeneous exhibiting a multi-scaling law," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 587(C).
    11. Ye, Yucheng & Xu, Shuqi & Mariani, Manuel Sebastian & Lü, Linyuan, 2022. "Forecasting countries' gross domestic product from patent data," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    12. Quan M. Tran & Hien D. Nguyen & Tai Huynh & Kha V. Nguyen & Suong N. Hoang & Vuong T. Pham, 2022. "Measuring the influence and amplification of users on social network with unsupervised behaviors learning and efficient interaction-based knowledge graph," Journal of Combinatorial Optimization, Springer, vol. 44(4), pages 2919-2945, November.
    13. Mitra, Tushar & Hassan, Md. Kamrul, 2022. "A weighted planar stochastic lattice with scale-free, small-world and multifractal properties," Chaos, Solitons & Fractals, Elsevier, vol. 154(C).
    14. Feng, Zhidan & Song, Huimin & Qi, Xingqin, 2024. "A novel algorithm for the generalized network dismantling problem based on dynamic programming," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).
    15. Annamaria Ficara & Francesco Curreri & Giacomo Fiumara & Pasquale De Meo & Antonio Liotta, 2022. "Covert Network Construction, Disruption, and Resilience: A Survey," Mathematics, MDPI, vol. 10(16), pages 1-43, August.
    16. Alexandru Topîrceanu, 2022. "Benchmarking Cost-Effective Opinion Injection Strategies in Complex Networks," Mathematics, MDPI, vol. 10(12), pages 1-16, June.
    17. Chen, Peng & Qi, Mingze & Yan, Liang & Duan, Xiaojun, 2024. "Diffusion capacity analysis of complex network based on the cluster distribution," Chaos, Solitons & Fractals, Elsevier, vol. 178(C).
    18. Hou, Lei, 2022. "Network versus content: The effectiveness in identifying opinion leaders in an online social network with empirical evaluation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 592(C).
    19. Xinyu Huang & Dongming Chen & Dongqi Wang & Tao Ren, 2020. "MINE: Identifying Top- k Vital Nodes in Complex Networks via Maximum Influential Neighbors Expansion," Mathematics, MDPI, vol. 8(9), pages 1-25, August.
    20. Fan, Dongming & Sun, Bo & Dui, Hongyan & Zhong, Jilong & Wang, Ziyao & Ren, Yi & Wang, Zili, 2022. "A modified connectivity link addition strategy to improve the resilience of multiplex networks against attacks," Reliability Engineering and System Safety, Elsevier, vol. 221(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:chsofr:v:166:y:2023:i:c:s0960077922011316. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.