Table 1:

Summary statistics for detecting differential networks, including estimated power of correct detection (with respect to network types), true positives for edge connections and directions (TPCD), false positives (FP), and correct connections (CC) across 100 MC replicates along with 95% empirical intervals (EI).

Sample size (nx = ny)	Power (%)	TPCD (95% EI)	FP (95% EI)	CC (95% EI)
Underlying networks: identical networks (p = 10 nodes, |E0c| = 10 edges)
50	94.9	0.999 (0.998, 1.0)	0.020 (0.0, 0.086)	0.984 (0.933, 1.0)
100	98.2	0.999 (0.999, 1.0)	0.021 (0.0, 0.086)	0.984 (0.932, 1.0)
200	98.9	0.999 (0.998, 1.0)	0.013 (0.0, 0.072)	0.990 (0.944, 1.0)
Underlying networks: identical networks (p = 10 nodes, |E0c| = 20 edges)
50	90.0	0.984 (0.900, 1.0)	0.199 (0.0, 0.622)	0.882 (0.644, 1.0)
100	91.7	0.996 (0.950, 1.0)	0.199 (0.0, 0.560)	0.887 (0.676, 1.0)
200	88.7	0.993 (0.965, 1.0)	0.230 (0.0, 0.640)	0.869 (0.629, 1.0)
Underlying networks: differential networks (p = 10 nodes, |E0x| = 5, |E0y | = 10 edges)
50	99.9	X : 0.998 (0.999, 1.0)	0.017 (0.00, 0.075)	0.984 (0.933, 1.0)
		Y : 0.977 (0.80, 1.0)	0.047 (0.00, 0.200)	0.958 (0.844, 1.0)
100	99.9	X : 1.0 (0.999, 1.0)	0.016 (0.00, 0.088)	0.986 (0.921, 1.0)
		Y : 0.992 (0.90, 1.0)	0.045 (0.00, 0.329)	0.963 (0.744, 1.0)
200	99.9	X : 1.0 (0.999, 1.0)	0.013 (0.00, 0.063)	0.988 (0.944, 1.0)
		Y : 0.996 (0.998, 1.0)	0.048 (0.00, 0.287)	0.962 (0.776, 1.00)
Underlying networks: differential networks (p = 10 nodes, |E0x| = 20, |E0y| = 10 edges)
50	99.9	X : 0.979 (0.874, 1.0)	0.218 (0.0, 0.560)	0.869 (0.667, 1.0)
		Y : 0.976 (0.90, 1.0)	0.039 (0.0, 0.171)	0.964 (0.867, 1.0)
100	99.9	X : 0.993 (0.950, 1.0)	0.213 (0.0, 0.600)	0.879 (0.633, 1.0)
		Y : 0.991 (0.90, 1.0)	0.033 (0.0, 0.186)	0.972 (0.855, 1.0)
200	99.9	X : 0.993 (0.950, 1.0)	0.232 (0.0, 0.600)	0.868 (0.644, 1.0)
		Y : 0.998 (0.997, 1.0)	0.040 (0.0, 0.230)	0.967 (0.821, 1.0)