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Introduction to the Modeling and Analysis of Complex Systems

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18.5. MEAN-FIELD APPROXIMATION ON RANDOM NETWORKS 417here we assume synchronous, simultaneous updating, in order <strong>to</strong> make <strong>the</strong> mean-fieldapproximation more similar <strong>to</strong> <strong>the</strong> approximation we applied <strong>to</strong> CA.For <strong>the</strong> mean-field approximation, we need <strong>to</strong> represent <strong>the</strong> state <strong>of</strong> <strong>the</strong> system by amacroscopic variable, i.e., <strong>the</strong> probability (= density, fraction) <strong>of</strong> <strong>the</strong> infected nodes in <strong>the</strong>network (say, q) in this case, <strong>and</strong> <strong>the</strong>n describe <strong>the</strong> temporal dynamics <strong>of</strong> this variableby assuming that this probability applies <strong>to</strong> everywhere in <strong>the</strong> network homogeneously(i.e., <strong>the</strong> “mean field”). In <strong>the</strong> following sections, we will discuss how <strong>to</strong> apply <strong>the</strong> meanfieldapproximation <strong>to</strong> two different network <strong>to</strong>pologies: r<strong>and</strong>om networks <strong>and</strong> scale-freenetworks.18.5 Mean-Field Approximation on R<strong>and</strong>om NetworksIf we can assume that <strong>the</strong> network <strong>to</strong>pology is r<strong>and</strong>om with connection probability p e ,<strong>the</strong>n infection occurs with a joint probability <strong>of</strong> three events: that a node is connected <strong>to</strong>ano<strong>the</strong>r neighbor node (p e ), that <strong>the</strong> neighbor node is infected by <strong>the</strong> disease (q), <strong>and</strong> that<strong>the</strong> disease is actually transmitted <strong>to</strong> <strong>the</strong> node (p i ). Therefore, 1 − p e qp i is <strong>the</strong> probabilitythat <strong>the</strong> node is not infected by ano<strong>the</strong>r node. In order for a susceptible node <strong>to</strong> remainsusceptible <strong>to</strong> <strong>the</strong> next time step, it must avoid infection in this way n − 1 times, i.e., forall o<strong>the</strong>r nodes in <strong>the</strong> network. The probability for this <strong>to</strong> occur is thus (1 − p e qp i ) n−1 .Using this result, all possible scenarios <strong>of</strong> state transitions can be summarized as shownin Table 18.1.Table 18.1: Possible scenarios <strong>of</strong> state transitions in <strong>the</strong> network SIS model.Current state Next state Probability <strong>of</strong> this transition0 (susceptible) 0 (susceptible) (1 − q)(1 − p e qp i ) n−10 (susceptible) 1 (infected) (1 − q) (1 − (1 − p e qp i ) n−1 )1 (infected) 0 (susceptible) qp r1 (infected) 1 (infected) q(1 − p r )We can combine <strong>the</strong> probabilities <strong>of</strong> <strong>the</strong> transitions that turn <strong>the</strong> next state in<strong>to</strong> 1, <strong>to</strong>write <strong>the</strong> following difference equation for q t (whose subscript is omitted on <strong>the</strong> right h<strong>and</strong>

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