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Table 11 Realism results for the Thurman Office 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 15.00 15.00 0.00 0.00 0.00 15.00 0.00 0.00 0.00 15.00 0.00 0.00 0.00
Links 33.00 25.53 7.47 224.00 42.07 33.00 0.00 0.00 0.00 33.00 0.00 0.00 0.00
Components 1.00 1.07 0.07 2.00 1.41 1.00 0.00 0.00 0.00 1.03 0.03 1.00 1.00
Network density 0.31 0.24 0.07 2.13 0.40 0.31 0.00 0.00 0.00 0.31 0.00 0.00 0.00
Average degree 4.40 3.40 1.00 29.87 5.61 4.40 0.00 0.00 0.00 4.40 0.00 0.00 0.00
Standard deviation degree 2.53 1.78 0.75 22.57 4.29 3.03 0.50 15.07 2.91 2.94 0.41 12.43 2.39
Global cluster coefficient 0.52 0.25 0.27 7.96 1.51 0.47 0.05 1.47 0.32 0.47 0.05 1.46 0.32
Average cluster coefficient 0.48 0.28 0.20 6.34 1.28 0.73 0.25 7.49 1.38 0.71 0.23 6.90 1.29
Mean path length 1.88 2.27 0.39 11.83 3.16 1.80 0.08 2.47 0.50 1.86 0.02 3.67 1.63
Communities 3.00 3.77 0.77 31.00 7.42 3.87 0.87 48.00 10.10 3.63 0.63 35.00 8.43
Gini coefficient 0.18 0.22 0.05 2.44 0.57 0.28 0.11 4.03 0.80 0.28 0.11 3.48 0.76
Average betweenness 6.13 8.02 1.89 56.67 11.24 5.61 0.53 17.27 3.53 5.59 0.54 16.73 3.67
Maximum betweenness 37.25 28.64 8.61 282.45 57.01 47.22 9.97 312.71 64.49 47.27 10.03 307.51 65.38
Average closeness 0.04 0.03 0.01 0.14 0.03 0.04 0.00 0.06 0.01 0.04 0.00 0.06 0.01
Minimum closeness 0.03 0.02 0.01 0.18 0.04 0.03 0.00 0.06 0.01 0.03 0.00 0.04 0.01
Average eigencentrality 0.53 0.54 0.01 1.08 0.24 0.49 0.04 1.21 0.24 0.49 0.04 1.21 0.24
Minimum eigencentrality 0.11 0.12 0.01 1.39 0.29 0.15 0.04 1.46 0.29 0.15 0.04 1.68 0.32
Network radius 2.00 2.67 0.67 20.00 4.47 2.00 0.00 0.00 0.00 2.00 0.00 0.00 0.00
Average eccentricity 2.80 3.48 0.68 20.33 4.09 2.67 0.13 7.13 1.45 2.65 0.15 5.93 1.31
Network diameter 3.00 4.30 1.30 39.00 7.68 3.27 0.27 8.00 2.83 3.10 0.10 3.00 1.73
  1. Boldfaced numbers indicate which algorithm performed better for a particular metric