TABLE II.

Numerical comparison of graph learning with Poisson noise (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]	0.226 ± 0.024	0.434 ± 0.027	0.154 ± 0.038	0.914 ± 0.019	0.870 ± 0.030	0.574 ± 0.069	0.907 ± 0.015
GLS-1 [11]	0.218 ± 0.024	0.427 ± 0.038	0.146 ± 0.032	0.845 ± 0.031	0.807 ± 0.035	0.460 ± 0.071	0.871 ± 0.018
GLS-2 [12]	0.246 ± 0.032	0.453 ± 0.045	0.179 ± 0.040	0.882 ± 0.025	0.839 ± 0.031	0.506 ± 0.073	0.891 ± 0.015
CGL [13]	0.202 ± 0.027	0.380 ± 0.031	0.142 ± 0.037	0.909 ± 0.020	0.861 ± 0.031	0.548 ± 0.073	0.900 ± 0.016
GLEN	0.129 ± 0.015	0.279 ± 0.039	0.068 ± 0.017	0.926 ± 0.018	0.890 ± 0.030	0.612 ± 0.084	0.918 ± 0.015
Stochastic Block Model
SCGL [56]	0.216 ± 0.022	0.403 ± 0.036	0.141 ± 0.027	0.930 ± 0.015	0.897 ± 0.027	0.643 ± 0.074	0.911 ± 0.016
GLS-1 [11]	0.217 ± 0.024	0.409 ± 0.039	0.140 ± 0.028	0.841 ± 0.033	0.817 ± 0.032	0.493 ± 0.071	0.870 ± 0.017
GLS-2 [12]	0.243 ± 0.026	0.432 ± 0.040	0.173 ± 0.029	0.882 ± 0.031	0.856 ± 0.053	0.569 ± 0.079	0.891 ± 0.018
CGL [13]	0.207 ± 0.022	0.370 ± 0.027	0.146 ± 0.029	0.924 ± 0.018	0.889 ± 0.028	0.633 ± 0.070	0.909 ± 0.018
GLEN	0.116 ± 0.015	0.234 ± 0.044	0.063 ± 0.013	0.941 ± 0.014	0.920 ± 0.025	0.701 ± 0.071	0.925 ± 0.015
Watts-Strogatz Model
SCGL [56]	0.120 ± 0.013	0.214 ± 0.023	0.072 ± 0.014	0.938 ± 0.020	0.930 ± 0.026	0.747 ± 0.061	0.933 ± 0.025
GLS-1 [11]	0.176 ± 0.017	0.318 ± 0.031	0.101 ± 0.019	0.878 ± 0.035	0.880 ± 0.037	0.638 ± 0.072	0.896 ± 0.019
GLS-2 [12]	0.208 ± 0.018	0.357 ± 0.030	0.137 ± 0.020	0.900 ± 0.034	0.895 ± 0.033	0.665 ± 0.066	0.896 ± 0.019
CGL [13]	0.184 ± 0.016	0.306 ± 0.023	0.129 ± 0.019	0.950 ± 0.015	0.941 ± 0.024	0.769 ± 0.063	0.806 ± 0.015
GLEN	0.100 ± 0.012	0.172 ± 0.019	0.065 ± 0.013	0.960 ± 0.011	0.960 ± 0.019	0.813 ± 0.055	0.941 ± 0.015