Fig. 3

Community detection on nPSO networks (3rd setting: C fixed; N, γ, m and T changing). Synthetic networks have been generated using the nPSO model with parameters N = [500, 1000] (network size) γ = [2, 2.5, 3] (power-law degree distribution exponent), m = [2, 4, 6, 8, 10, 12, 14, 16] (half of average degree), T = [0.1, 0.5] (temperature, inversely related to the clustering coefficient) and C = 9 (number of communities). For each combination of parameters, 10 networks have been generated. For each network the community detection methods have been executed and the communities detected have been compared to the annotated ones computing the Normalized Mutual Information (NMI). The plots report for each parameter combination the mean NMI and standard error over the random repetitions and show that MCL improves its performance with the increase of γ for middle and large size networks