Predicting Citation Counts for Academic Literature Using Graph Pattern Mining

  • Nataliia Pobiedina
  • Ryutaro Ichise
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8482)

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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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Nataliia Pobiedina
    • 1
  • Ryutaro Ichise
    • 2
  1. 1.Institute of Software Technology and Interactive SystemsVienna University of TechnologyAustria
  2. 2.Principles of Informatics Research DivisionNational Institute of InformaticsJapan

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