Research Article
MIC as an Appropriate Method to Construct the Brain Functional Network
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.
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