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Table 20 Realism results for the Krebs Fortune 500 IT Department (Advice) social network

From: Heuristic methods for synthesizing realistic social networks based on personality compatibility

Metrics T \( \overline{F} \) |T- \( \overline{F} \) | L1(F) L2(F) \( \overline{P} \) |T-\( \overline{P} \)| L1(P) L2(P) \( \overline{M} \) |T-\( \overline{M} \)| L1(M) L2(M)
Nodes 56.00 56.00 0.00 0.00 0.00 56.00 0.00 0.00 0.00 56.00 0.00 0.00 0.00
Links 203.00 182.53 20.47 614.00 113.61 203.00 0.00 0.00 0.00 203.00 0.00 0.00 0.00
Components 2.00 2.00 0.00 0.00 0.00 2.00 0.00 0.00 0.00 2.00 0.00 0.00 0.00
Network density 0.13 0.12 0.01 0.40 0.07 0.13 0.00 0.00 0.00 0.13 0.00 0.00 0.00
Average degree 7.25 6.52 0.73 21.93 4.06 7.25 0.00 0.00 0.00 7.25 0.00 0.00 0.00
Standard deviation degree 4.18 3.45 0.73 22.02 4.12 4.70 0.52 15.57 3.04 4.78 0.60 17.88 3.34
Global cluster coefficient 0.35 0.15 0.20 5.95 1.09 0.28 0.07 2.14 0.40 0.29 0.06 1.81 0.34
Average cluster coefficient 0.42 0.16 0.27 7.99 1.47 0.43 0.00 0.46 0.11 0.43 0.00 0.42 0.10
Mean path length 4.29 4.22 0.06 1.87 0.37 4.15 0.14 4.12 0.77 4.15 0.14 4.20 0.78
Communities 8.00 11.10 3.10 107.00 22.96 9.30 1.30 61.00 13.53 8.67 0.67 68.00 15.75
Gini coefficient 0.43 0.46 0.03 1.63 0.34 0.48 0.05 1.71 0.38 0.45 0.02 1.33 0.30
Average betweenness 36.32 34.61 1.71 51.38 10.04 32.55 3.78 113.30 21.17 32.47 3.85 115.46 21.49
Maximum betweenness 262.14 169.51 92.63 2778.80 524.32 457.03 194.89 5846.64 1154.39 500.81 238.67 7160.13 1355.01
Average closeness 0.01 0.01 0.00 0.01 0.00 0.01 0.00 0.02 0.00 0.01 0.00 0.02 0.00
Minimum closeness 0.01 0.01 0.00 0.01 0.00 0.01 0.00 0.01 0.00 0.01 0.00 0.01 0.00
Average eigencentrality 0.32 0.40 0.08 2.47 0.48 0.32 0.01 0.75 0.17 0.31 0.01 0.69 0.15
Minimum eigencentrality 0.02 0.05 0.03 0.80 0.17 0.03 0.01 0.28 0.06 0.03 0.01 0.32 0.07
Network radius 3.00 3.00 0.00 0.00 0.00 2.80 0.20 6.00 2.45 2.80 0.20 6.00 2.45
Average eccentricity 3.66 3.56 0.10 4.09 0.89 3.45 0.21 6.93 1.47 3.48 0.19 6.84 1.43
Network diameter 5.00 4.60 0.40 12.00 3.46 4.47 0.53 16.00 4.00 4.40 0.60 18.00 4.24
  1. Boldfaced numbers indicate which algorithm performed better for a particular metric