Following the algorithm described in Eigenfactor Article Influence Algorithm, a new matrix is calculated as:
T
S N l e (12)
苑
Where α andβ are partial coefficients, α + β =1, and is a row vector of 1 s. We find out
the greatest eigenvalue of matrix S, and the corresponding eigenvector is defined as the evaluation vector v. Through the interpretation given by Eigenfactor, [19]the values of each element of the leading vector represent an average fraction of time spent on each article[19]. However, the situation here is a little different in three respects: (1) the matrix N is a symmetrical matrix; (2) The individual influence vector here is the corresponding individual search results divided by the sum of search results; (3) the relative magnitude of α and β cannot be determined by previous data.
Finally, the evaluation vector is sorted in descending order. The location of element is the ranking of corresponding coach i.
4.6. Solutions to Model II
4.6.1. Basketball
Searching and selecting data
First, we choose 100 coaches and their evaluation index data as our candidate pool. Secondly, we rank them by comprehensively considering the total number of wins and the winning-percentage. In this way, we get top 20 coaches from the candidates. The evaluation system is based on the selected 20 data.
Through Google search results, we obtain the original search result vector a(the third column in Table A5) and the original cross-reference matrix Z(Table A6). The influence coefficient of time
We use linear regression method, and the result turns out to be: k 0.0368,b 62.2719.
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