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Ranking scientific publications with similarity-preferential mechanism

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Abstract

Along with the advance of internet and fast updating of information, nowadays it is much easier to search and acquire scientific publications. To identify the high quality articles from the paper ocean, many ranking algorithms have been proposed. One of these methods is the famous PageRank algorithm which was originally designed to rank web pages in online systems. In this paper, we introduce a preferential mechanism to the PageRank algorithm when aggregating resource from different nodes to enhance the effect of similar nodes. The validation of the new method is performed on the data of American Physical Society journals. The results indicate that the similarity-preferential mechanism improves the performance of the PageRank algorithm in terms of ranking effectiveness, as well as robustness against malicious manipulations. Though our method is only applied to citation networks in this paper, it can be naturally used in many other real systems, such as designing search engines in the World Wide Web and revealing the leaderships in social networks.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China under Grant Nos. 61374175, 61174150 and 11547188, the Young Scholar Program of Beijing Normal University (2014NT38).

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Authors and Affiliations

  1. School of systems science, Beijing Normal University, Beijing, 100875, People’s Republic of China

    Jianlin Zhou, An Zeng, Ying Fan & Zengru Di

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  1. Jianlin Zhou
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  2. An Zeng
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  3. Ying Fan
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  4. Zengru Di
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Correspondence to An Zeng or Ying Fan.

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Zhou, J., Zeng, A., Fan, Y. et al. Ranking scientific publications with similarity-preferential mechanism. Scientometrics 106, 805–816 (2016). https://doi.org/10.1007/s11192-015-1805-1

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