Selected article for: "infection probability and susceptible neighbor"

Author: Na Zhao; Jian Wang; Yong Yu; Jun-Yan Zhao; Duan-Bing Chen
Title: Spreading predictability in complex networks
  • Document date: 2020_1_28
  • ID: 635lbedk_3
    Snippet: where Γ u is the neighbors of node u and infected probability µ is estimated by IAIP 47 model (Iterative Algorithm for estimating the Infection Probability) [23] . Since an 48 infected node always attempts to infect its susceptible neighbor once time and a 49 recovered node doesn't infect any of its susceptible neighbor, so, in Eq. (1), for node v, 50 it is reasonable to assume that P v (t) = 1 for infected node and P v (t) = 0 for recovered 51.....
    Document: where Γ u is the neighbors of node u and infected probability µ is estimated by IAIP 47 model (Iterative Algorithm for estimating the Infection Probability) [23] . Since an 48 infected node always attempts to infect its susceptible neighbor once time and a 49 recovered node doesn't infect any of its susceptible neighbor, so, in Eq. (1), for node v, 50 it is reasonable to assume that P v (t) = 1 for infected node and P v (t) = 0 for recovered 51 node. For susceptible node u, the probability to be infected at time t is P u (t).

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