Fig 9. Simulation results of our model and truncated power law.

For each simulation, we generate a random sequence r₁, …, r_N (r_i ∈ [0, 1]) and corresponding strength values s₁, …s_N are determined with using the cumulative distribution r_i = P_c(s_i), where N is equivalent to the number of nodes in the experiment. A Profile of the cumulative distribution. The cumulative distributions, which follow our model and the truncated power law, are shown. The parameter sets are fitting results of the r_c = 0.4 for the raw data (Fig 5C and 5D). They are γ = 2.22, x_max = 33.5, and x_min = 0.546 for our model and α = 2.17 and x_c = 5.67 for the truncated power law. B Δ < s >, simulation differences from the experimental data. The simulation results of the average, < s >, are compared to the experimental data with taking the difference, Δ < s > = < s >_simulation − < s >_experiment. We use the parameter sets determined by the maximum likelihood method applied to the raw data and the sampling data with n = 50, 100 for each threshold r_c (Fig 7). Then we repeat 986 times, the number of experimental datasets, and take the average < s > of these simulation results for each condition.