Table 6.

C: robustness in the importance of all nodes. We used degree centrality (DC) and Shannon-Parry centrality (SPC) to measure the importance of node in network and put the importance vector calculated in the same method from different sample as the row to construct matrix, so each method obtained a 13 ∗ 90 matrix. The value in the table was the sum of Euclidean distance between different row vectors for one method. D: robustness in the ranking of all nodes in importance. We used degree and Parry measure to measure the importance of node in network and put the importance vector calculated in the same method from different sample as the row to construct matrix, so each method obtained a 13 ∗ 90 matrix. Then we got the rank of node in each row, so each method got a 13 ∗ 90 rank matrix. The value in the table was the sum of Euclidean distance between different row vectors from rank matrix of one method. If this sum of distance was smaller, corresponding method had better robustness. In the aspect C, MIC is ranked the second and is only bigger than PCF. In the aspect D, MIC had the smallest sum of distance, so MIC had better robustness than other methods.

Aspects		CF	PCF	MIC	MI	WCF	CH
C	DC	9148.4	7282.8	8344.8	9536.6	9750.4	10119
	SPC	8.30	6.54	7.59	8.08	8.90	9.45
D	DC	24910	26483	24588	24889	25705	26544
	SPC	24669	26574	24435	24677	25600	26680