Acquaintance immunization with limited knowledge of network structure

Yangyang Liu, Qiangjuan Huang, Gaogao Dong, Meng Yao, Louis M. Shekhtman, H. Eugene Stanley

Research output: Contribution to journalArticlepeer-review

Abstract

Optimal and efficient immunization of large networks remains a challenging task. Many theories and approaches have been suggested, however most of them require complete knowledge of the underlying network structure. Here, we study a targeted immunization strategy that incorporates the fact that there is often limited knowledge on the network structure. Previous work has suggested ‘acquaintance’ immunization, where rather than selecting a random individual to immunize, an individual is selected and then one of their acquaintances is immunized. Here, we generalize acquaintance immunization to the case where rather than selecting a random acquaintance, we examine the degrees of n acquaintances and immunize the one with the highest degree. We develop and solve an analytic framework for this model and verify our model with extensive numerical simulations. We determine the critical percolation threshold pc and the size of the giant component, P ∞ , for arbitrary degree distributions. We also consider our immunization strategy on real-world networks and determine the variation of pc with increasing n. We find that our new approach improves on both acquaintance immunization and random immunization using limited knowledge.

Original languageEnglish
Article number093017
JournalNew Journal of Physics
Volume25
Issue number9
DOIs
StatePublished - 1 Sep 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2023 The Author(s). Published by IOP Publishing Ltd on behalf of the Institute of Physics and Deutsche Physikalische Gesellschaft.

Funding

This research is supported by grants from the National Natural Science Foundation of China (Nos: 62373169, 61973143), National Statistical Science Research Project (No: 2022LZ03), Special Project of Emergency Management Institute of Jiangsu University (No. KY-A-08).

FundersFunder number
Special Project of Emergency Management Institute of Jiangsu UniversityKY-A-08
National Natural Science Foundation of China61973143, 2022LZ03, 62373169

    Keywords

    • complex networks
    • network immunization
    • percolation theory

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