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Efficient network immunization under limited knowledge

National science Review 2021年第1期8卷 127-134页

作者： Yangyang Liu Hillel Sanhedrai Gao Gao Dong Louis M.Shekhtman Fan Wang Sergey V.Buldyrev Shlomo Havlin Department of Systems science National University of Defense Technology Department of Physics Bar-Ilan University Faculty of science Jiangsu University Network science Institute Northeastern University Department of Physics Yeshiva University

Targeted immunization of centralized nodes in large-scale networks has attracted significant ***, in real-world scenarios, knowledge and observations of the network may be limited, thereby precluding a full assessment of the optimal nodes to immunize（or quarantine） in order to avoid epidemic spreading such as that of the current coronavirus disease（COVID-19） epidemic. Here, we study a novel immunization strategy where only n nodes are observed at a time and the most central among these n nodes is immunized. This process can globally immunize a network. We find that even for small n（≈10） there is significant improvement in the immunization（quarantine）, which is very close to the levels of immunization with full knowledge. We develop an analytical framework for our method and determine the critical percolation threshold p;and the size of the giant component P∞ for networks with arbitrary degree distributions P（k）. In the limit of n →∞ we recover prior work on targeted immunization, whereas for n = 1 we recover the known case of random immunization. Between these two extremes, we observe that, as n increases,p;increases quickly towards its optimal value under targeted immunization with complete information. In particular, we find a new general scaling relationship between |p;（∞）-p;（n）| and n as |p;（∞）-p;（n）| ～n;exp（-αn）. For scale-free（SF） networks, where P（k）～ k;, 2 < γ < 3, we find that p;has a transition from zero to nonzero when n increases from n = 1 to O（log N）(where N is the size of the network). Thus, for SF networks, having knowledge of ≈log N nodes and immunizing the most optimal among them can dramatically reduce epidemic spreading. We also demonstrate our limited knowledge immunization strategy on several real-world networks and confirm that in these real networks, p;increases significantly even for small n.

关键词： percolation complex networks network immunization critical phenomena

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