TABLE IV.

Numerical comparison of graph learning with Poisson noise (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]	0.263 ± 0.025	0.774 ± 0.062	0.161 ± 0.026	0.741 ± 0.017	0.672 ± 0.017	0.228 ± 0.029	0.776 ± 0.009
GLS-1 [11]	0.192 ± 0.010	0.614 ± 0.019	0.095 ± 0.016	0.754 ± 0.015	0.678 ± 0.017	0.230 ± 0.030	0.782 ± 0.011
GLS-2 [12]	0.199 ± 0.009	0.612 ± 0.022	0.111 ± 0.013	0.760 ± 0.015	0.683 ± 0.016	0.236 ± 0.031	0.791 ± 0.009
CGL [13]	0.206 ± 0.008	0.613 ± 0.021	0.122 ± 0.013	0.754 ± 0.016	0.678 ± 0.018	0.230 ± 0.031	0.791 ± 0.008
GLEN	0.177 ± 0.006	0.610 ± 0.030	0.060 ± 0.008	0.767 ± 0.015	0.690 ± 0.017	0.248 ± 0.031	0.800 ± 0.007
Stochastic Block Model
SCGL [56]	0.213 ± 0.016	0.608 ± 0.046	0.117 ± 0.016	0.789 ± 0.021	0.713 ± 0.022	0.324 ± 0.038	0.796 ± 0.011
GLS-1 [11]	0.195 ± 0.009	0.566 ± 0.019	0.101 ± 0.015	0.796 ± 0.020	0.717 ± 0.021	0.327 ± 0.033	0.798 ± 0.011
GLS-2 [12]	0.202 ± 0.009	0.555 ± 0.022	0.121 ± 0.012	0.801 ± 0.018	0.726 ± 0.020	0.342 ± 0.033	0.808 ± 0.009
CGL [13]	0.210 ± 0.009	0.556 ± 0.021	0.134 ± 0.012	0.800 ± 0.020	0.718 ± 0.021	0.331 ± 0.038	0.806 ± 0.010
GLEN	0.169 ± 0.007	0.523 ± 0.028	0.066 ± 0.010	0.811 ± 0.019	0.734 ± 0.021	0.352 ± 0.037	0.815 ± 0.009
Watts-Strogatz Model
SCGL [56]	0.147 ± 0.014	0.249 ± 0.021	0.099 ± 0.014	0.963 ± 0.010	0.935 ± 0.016	0.813 ± 0.032	0.908 ± 0.014
GLS-1 [11]	0.192 ± 0.015	0.337 ± 0.024	0.120 ± 0.016	0.856 ± 0.028	0.853 ± 0.024	0.664 ± 0.040	0.866 ± 0.015
GLS-2 [12]	0.220 ± 0.017	0.367 ± 0.025	0.152 ± 0.018	0.886 ± 0.023	0.874 ± 0.020	0.697 ± 0.036	0.866 ± 0.014
CGL [13]	0.196 ± 0.016	0.319 ± 0.020	0.140 ± 0.019	0.965 ± 0.009	0.935 ± 0.015	0.814 ± 0.032	0.904 ± 0.016
GLEN	0.110 ± 0.014	0.180 ± 0.021	0.078 ± 0.014	0.975 ± 0.008	0.967 ± 0.013	0.884 ± 0.032	0.922 ± 0.012