A general deep learning framework for network reconstruction and dynamics learning

Applied Network Science

Table 1 Results with Boolean Network

Node Num	State	LSTM	NRI				GGN
		ACC(dyn)	ACC(net)	TPR	FPR	ACC(dyn)	ACC(net)	TPR	FPR	ACC(dyn)
10	non-chaotic	0.841	0.568	0.422	0.395	0.820	0.991	1	0.008	0.694
10	chaotic	0.789	0.481	0.458	0.465	0.528	0.994	0.983	0	0.693
30	non-chaotic	0.912	0.409	0.590	0.591	0.798	0.926	0.476	0.036	0.948
30	chaotic	0.765	0.460	0.549	0.547	0.721	0.9	0.601	0.034	0.699
100	non-chaotic	0.933	-	-	-	-	0.84	0.505	0.153	0.982
100	chaotic	0.796	-	-	-	-	0.957	0.25	0.013	0.7483