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Citation count prediction as a link prediction problem

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Abstract

The citation count is an important factor to estimate the relevance and significance of academic publications. However, it is not possible to use this measure for papers which are too new. A solution to this problem is to estimate the future citation counts. There are existing works, which point out that graph mining techniques lead to the best results. We aim at improving the prediction of future citation counts by introducing a new feature. This feature is based on frequent graph pattern mining in the so-called citation network constructed on the basis of a dataset of scientific publications. Our new feature improves the accuracy of citation count prediction, and outperforms the state-of-the-art features in many cases which we show with experiments on two real datasets.

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  1. http://www-kdd.isti.cnr.it/GERM/

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Authors and Affiliations

  1. Institute of Software Technology and Interactive Systems, Vienna University of Technology, Vienna, Austria

    Nataliia Pobiedina

  2. Principles of Informatics Research Division, National Institute of Informatics, Tokyo, Japan

    Ryutaro Ichise

Authors
  1. Nataliia Pobiedina
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  2. Ryutaro Ichise
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Corresponding author

Correspondence to Nataliia Pobiedina.

Additional information

This is an extended and enhanced version of the results published in [1].

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Pobiedina, N., Ichise, R. Citation count prediction as a link prediction problem. Appl Intell 44, 252–268 (2016). https://doi.org/10.1007/s10489-015-0657-y

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