Journal influence evaluation method based on academic big data

An influential and big data technology, which is applied in the fields of electronic digital data processing, special data processing applications, unstructured text data retrieval, etc., can solve the problems of single evaluation index and inability to evaluate the influence of journals across fields

Inactive Publication Date: 2017-03-08
DALIAN UNIV OF TECH
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However, the above algorithm also has problems such as too single evaluat

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  • Journal influence evaluation method based on academic big data
  • Journal influence evaluation method based on academic big data
  • Journal influence evaluation method based on academic big data

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Embodiment Construction

[0033] In order to make the purpose, technical solution and advantages of the present invention clearer, the specific implementation manners of the present invention will be further described in detail below.

[0034] An embodiment of the present invention provides a method for evaluating the influence of journals based on academic big data, the method comprising:

[0035] Step 1: Select the DBLP and MAG datasets as the experimental datasets of this method, and preprocess the DBLP and MAG datasets.

[0036] The present invention refers to the ranking of periodicals recommended by the China Computer Federation in 2015, and selects 42 periodicals in the field of "artificial intelligence" as evaluation criteria (the MAG data set selects 45 periodicals in the field of artificial intelligence). The two datasets were preprocessed according to the experimental requirements, figure 2 A flow chart for processing the MAG dataset is given.

[0037] The information of the two datasets ...

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Abstract

The invention discloses a journal influence evaluating method based on academic big data. According to the method, Average influence of all theses in every journal is used for representing the influence of the journal. A method for calculating the journal influence is a PageRank-based algorithm, namely, AR-PJageRank, the algorithm takes a similarity, an author H index and other factors between a thesis and another thesis referring to the thesis into consideration, AR makes the algorithm be a weighting algorithm, and in the algorithm iteration process, when all the theses vote a thesis cited by the theses, different contributions can be given due to the similarities between the thesis and the referenced thesis; PageRank makes the importance of each thesis greatly increased, an H index of each thesis is calculated through the sum of the author H indexes, and the H index serves as a label of the thesis so that difference of each thesis can gradually emerge in the calculation process. Accordingly, the method can accurately distinguish the influence of each thesis, and finally the influence of each thesis is obtained.

Description

technical field [0001] The invention relates to a method for evaluating the influence of a journal based on academic big data in the academic field, in particular to a method for evaluating the influence of a periodical combining a PageRank algorithm and an H index. Background technique [0002] With the continuous development of science and technology, more and more people are engaged in scientific research. Their research results are usually presented in the form of academic papers, patents, books, etc. A large number of scientific researchers have generated hundreds of millions of data. These data are collectively referred to as academic big data. Academic big data has a wide range of uses, and journal impact assessment is an important application of academic big data. The influence of a journal is based on the academic level and academic characteristics of the journal, and is a comprehensive effect reflected by the social credibility. The quality of the journal determin...

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/335G06F16/35
Inventor 夏锋白晓梅宁兆龙刘号真孔祥杰
Owner DALIAN UNIV OF TECH
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