Table 2.

Simulation results for data generated based on the Watt-Strogatz graph with different ratios of the number of individual-specific edges to the number of shared edges. Results are based on 100 replications. The numbers in the brackets are standard errors. CLIME: method of Cai, Liu and Luo (2011); GLASSO: graphical Lasso; JEMGM: method of Guo, Levina, Michailidis et al. (2011); fgland GGL: methods of Danaher, Wang and Witten (2014); MPE: proposed method.

Model(ρ)	Method			Performance
		L1	L2	LF	SEN	SPE	MCC
WS(0)	CLIME	29.80(0.23)	13.05(0.19)	87.71(1.96)	0.11(0.03)	0.99(0.00)	0.25(0.02)
	GLASSO	29.55(0.25)	12.35(0.08)	79.20(0.47)	0.08(0.01)	0.99(0.00)	0.20(0.01)
	JEMGM	29.59(0.46)	11.89(0.27)	74.32(2.50)	0.11(0.04)	1.00(0.00)	0.27(0.03)
	FGL	29.45(0.33)	12.43(0.13)	80.10(1.22)	0.10(0.02)	0.99(0.00)	0.23(0.03)
	GGL	29.65(0.23)	12.65(0.11)	80.53(0.67)	0.08(0.02)	1.00(0.00)	0.22(0.02)
	MPE	28.99(0.22)	12.72(0.12)	84.35(1.11)	0.16(0.01)	1.00(0.00)	0.34(0.01)
WS(1/4)	CLIME	42.70(0.35)	14.80(0.19)	102.03(2.08)	0.08(0.02)	0.98(0.01)	0.15(0.01)
	GLASSO	42.79(0.58)	14.25(0.07)	95.38(0.49)	0.04(0.00)	0.99(0.00)	0.09(0.00)
	JEMGM	43.06(0.60)	13.44(0.17)	84.35(1.75)	0.12(0.02)	0.98(0.00)	0.21(0.01)
	FGL	43.33(0.59)	14.31(0.06)	96.24(0.49)	0.06(0.01)	0.98(0.00)	0.11(0.01)
	GGL	42.84(0.54)	14.30(0.11)	95.95(0.95)	0.05(0.01)	0.99(0.00)	0.11(0.01)
	MPE	42.15(0.39)	14.60(0.11)	100.13(1.16)	0.10(0.00)	0.99(0.00)	0.23(0.01)
WS(1)	CLIME	63.27(0.35)	18.64(0.19)	128.83(1.94)	0.04(0.01)	0.99(0.00)	0.08(0.01)
	GLASSO	62.94(0.31)	17.85(0.11)	120.11(1.01)	0.02(0.01)	0.99(0.00)	0.04(0.01)
	JEMGM	63.67(0.85)	16.82(0.23)	107.53(2.20)	0.07(0.01)	0.98(0.00)	0.12(0.01)
	FGL	63.11(0.28)	17.86(0.07)	120.82(0.66)	0.02(0.00)	0.99(0.00)	0.04(0.00)
	GGL	63.13(0.35)	17.87(0.12)	120.31(1.05)	0.03(0.01)	0.99(0.01)	0.04(0.01)
	MPE	62.89(2.57)	18.18(0.47)	123.98(2.71)	0.08(0.09)	0.98(0.09)	0.15(0.01)