Table 2.

Cross validation results in terms of F-Measure, Precision, and Recall (± standard deviation) averaged on 3-folds.

	Identity	D	BC	CC	E	All-graphs
UNWEIGHTED
F-Measure	0.80 (±0.01)	0.39 (±0.03)	0.50 (±0.03)	0.47 (±0.06)	0.51 (±0.02)	0.56 (±0.04)
Precision	0.81 (±0.01)	0.33 (±0.03)	0.54 (±0.08)	0.47 (±0.11)	0.56 (±0.06)	0.57 (±0.04)
Recall	0.80 (±0.01)	0.48 (±0.04)	0.55 (±0.04)	0.55 (±0.04)	0.56 (±0.03)	0.60 (±0.04)
WEIGHTED
F-Measure	0.92 (±0.02)	0.64 (±0.01)	0.68 (±0.01)	0.64 (±0.02)	0.62 (±0.03)	0.74 (±0.02)
Precision	0.93 (±0.02)	0.70 (±0.02)	0.69 (±0.01)	0.66 (±0.01)	0.64 (±0.05)	0.76 (±0.02)
Recall	0.93 (±0.02)	0.65 (±0.02)	0.69 (±0.01)	0.64 (±0.02)	0.63 (±0.03)	0.75 (±0.03)

Rows report achieved results using unweighted graphs with unweighted features (upper) and using weighted graphs with weighted features (lower) [Degree (D), Betweenness Centrality (BC), Clustering Coefficient (CC), Local Efficiency (E), with all graph-metrics (all-graphs)] and without features (identity).