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_6
Snippet: In Eq. (1), the score P u (t) for susceptible node u will be converged to a unique steady 54 state denoted by P u (t c ) , where t c is the convergence time. The final score P u = P u (t c ) 55 is the probability to be infected of susceptible node while spreading achieves steady 56 state. of average over 10000 simulations, we use predictability χ and Pearson correlation Ï to 70 evaluate our model. These two metrics can be calculated by:.....
Document: In Eq. (1), the score P u (t) for susceptible node u will be converged to a unique steady 54 state denoted by P u (t c ) , where t c is the convergence time. The final score P u = P u (t c ) 55 is the probability to be infected of susceptible node while spreading achieves steady 56 state. of average over 10000 simulations, we use predictability χ and Pearson correlation Ï to 70 evaluate our model. These two metrics can be calculated by:
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