TABLE V.

Numerical comparison of graph learning with Binomial noises (N=50).

Method	${\mathbf{RE}}_{\mathbf{L}}\downarrow$	${\mathbf{RE}}_{edge}\downarrow$	${\mathbf{RE}}_{deg}\downarrow$	PR-AUC ↑	F-score ↑	NMI ↑	DeltaCon ↑
Erdős-Rényi Model
SCGL [56]	0.193 ± 0.010	0.647 ± 0.041	0.079 ± 0.009	0.803 ± 0.015	0.719 ± 0.017	0.291 ± 0.034	0.804 ± 0.007
GLS-1 [11]	0.168 ± 0.005	0.547 ± 0.024	0.077 ± 0.008	0.799 ± 0.014	0.718 ± 0.017	0.289 ± 0.031	0.807 ± 0.010
GLS-2 [12]	0.212 ± 0.010	0.560 ± 0.021	0.152 ± 0.015	0.800 ± 0.016	0.716 ± 0.017	0.289 ± 0.031	0.807 ± 0.007
CGL [13]	0.229 ± 0.016	0.558 ± 0.024	0.177 ± 0.020	0.795 ± 0.017	0.709 ± 0.017	0.282 ± 0.034	0.803 ± 0.007
GLEN	0.159 ± 0.005	0.540 ± 0.026	0.061 ± 0.006	0.804 ± 0.016	0.720 ± 0.016	0.294 ± 0.032	0.811 ± 0.008
Stochastic Block Model
SCGL [56]	0.165 ± 0.007	0.514 ± 0.032	0.064 ± 0.008	0.846 ± 0.016	0.764 ± 0.021	0.402 ± 0.035	0.821 ± 0.008
GLS-1 [11]	0.165 ± 0.007	0.484 ± 0.022	0.083 ± 0.011	0.833 ± 0.016	0.760 ± 0.019	0.398 ± 0.032	0.819 ± 0.008
GLS-2 [12]	0.215 ± 0.013	0.507 ± 0.023	0.160 ± 0.016	0.839 ± 0.015	0.760 ± 0.017	0.398 ± 0.034	0.817 ± 0.007
CGL [13]	0.224 ± 0.013	0.503 ± 0.020	0.174 ± 0.016	0.831 ± 0.017	0.749 ± 0.019	0.381 ± 0.039	0.815 ± 0.009
GLEN	0.150 ± 0.006	0.465 ± 0.026	0.059 ± 0.009	0.843 ± 0.016	0.765 ± 0.020	0.405 ± 0.038	0.826 ± 0.008
Watts-Strogatz Model
SCGL [56]	0.193 ± 0.015	0.290 ± 0.020	0.153 ± 0.015	0.969 ± 0.009	0.943 ± 0.015	0.829 ± 0.034	0.905 ± 0.014
GLS-1 [11]	0.228 ± 0.016	0.350 ± 0.019	0.177 ± 0.020	0.878 ± 0.025	0.876 ± 0.023	0.699 ± 0.041	0.861 ± 0.015
GLS-2 [12]	0.252 ± 0.020	0.380 ± 0.025	0.200 ± 0.022	0.901 ± 0.023	0.885 ± 0.021	0.715 ± 0.037	0.864 ± 0.016
CGL [13]	0.219 ± 0.013	0.351 ± 0.018	0.161 ± 0.017	0.967 ± 0.009	0.937 ± 0.016	0.817 ± 0.034	0.902 ± 0.015
GLEN	0.116 ± 0.015	0.177 ± 0.016	0.092 ± 0.017	0.976 ± 0.006	0.959 ± 0.013	0.865 ± 0.032	0.925 ± 0.017