TABLE III.

Numerical comparison of graph learning with Binomial noises (N=20).

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]	0.161 ± 0.020	0.342 ± 0.054	0.089 ± 0.022	0.904 ± 0.017	0.868 ± 0.029	0.575 ± 0.077	0.893 ± 0.025
GLS-1 [11]	0.202 ± 0.027	0.395 ± 0.035	0.133 ± 0.029	0.868 ± 0.024	0.840 ± 0.027	0.511 ± 0.064	0.887 ± 0.019
GLS-2 [12]	0.277 ± 0.030	0.449 ± 0.035	0.228 ± 0.030	0.891 ± 0.018	0.842 ± 0.024	0.513 ± 0.056	0.893 ± 0.014
CGL [13]	0.220 ± 0.028	0.338 ± 0.036	0.188 ± 0.030	0.914 ± 0.014	0.877 ± 0.025	0.593 ± 0.067	0.909 ± 0.014
GLEN	0.112 ± 0.012	0.234 ± 0.033	0.064 ± 0.014	0.935 ± 0.015	0.901 ± 0.027	0.641 ± 0.083	0.921 ± 0.015
Stochastic Block Model
SCGL [56]	0.171 ± 0.027	0.301 ± 0.038	0.121 ± 0.033	0.920 ± 0.021	0.895 ± 0.028	0.651 ± 0.071	0.906 ± 0.019
GLS-1 [11]	0.236 ± 0.037	0.412 ± 0.048	0.170 ± 0.041	0.859 ± 0.036	0.848 ± 0.037	0.556 ± 0.077	0.882 ± 0.021
GLS-2 [12]	0.302 ± 0.040	0.460 ± 0.047	0.251 ± 0.042	0.884 ± 0.030	0.846 ± 0.033	0.546 ± 0.068	0.884 ± 0.019
CGL [13]	0.229 ± 0.031	0.330 ± 0.038	0.198 ± 0.032	0.923 ± 0.019	0.896 ± 0.026	0.651 ± 0.062	0.911 ± 0.017
GLEN	0.112 ± 0.019	0.207 ± 0.036	0.074 ± 0.018	0.945 ± 0.015	0.929 ± 0.025	0.726 ± 0.067	0.929 ± 0.016
Watts-Strogatz Model
SCGL [56]	0.149 ± 0.018	0.229 ± 0.024	0.115 ± 0.019	0.951 ± 0.019	0.945 ± 0.028	0.779 ± 0.072	0.928 ± 0.023
GLS-1 [11]	0.213 ± 0.023	0.347 ± 0.033	0.152 ± 0.026	0.884 ± 0.034	0.895 ± 0.034	0.667 ± 0.070	0.895 ± 0.021
GLS-2 [12]	0.241 ± 0.028	0.372 ± 0.035	0.186 ± 0.030	0.919 ± 0.028	0.902 ± 0.032	0.673 ± 0.071	0.898 ± 0.020
CGL [13]	0.187 ± 0.022	0.284 ± 0.021	0.145 ± 0.028	0.955 ± 0.014	0.946 ± 0.024	0.782 ± 0.062	0.928 ± 0.018
GLEN	0.098 ± 0.012	0.161 ± 0.018	0.069 ± 0.013	0.964 ± 0.009	0.964 ± 0.022	0.827 ± 0.069	0.943 ± 0.018