Link prediction in citation networks

  • Authors:

  • Naoki Shibata;Yuya Kajikawa;Ichiro Sakata

  • Affiliations:

  • Innovation Policy Research Center, Graduate School of Engineering, The University of Tokyo, Tokyo, Japan;Innovation Policy Research Center, Graduate School of Engineering, The University of Tokyo, Tokyo, Japan;Innovation Policy Research Center, Graduate School of Engineering and Todai Policy Alternatives Research Institute, The University of Tokyo, Tokyo, Japan

  • Venue:
  • Journal of the American Society for Information Science and Technology
  • Year:
  • 2012

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Abstract

In this article, we build models to predict the existence of citations among papers by formulating link prediction for 5 large-scale datasets of citation networks. The supervised machine-learning model is applied with 11 features. As a result, our learner performs very well, with the F1 values of between 0.74 and 0.82. Three features in particular, link-based Jaccard coefficient, difference in betweenness centrality, and cosine similarity of term frequency-inverse document frequency vectors, largely affect the predictions of citations. The results also indicate that different models are required for different types of research areas--research fields with a single issue or research fields with multiple issues. In the case of research fields with multiple issues, there are barriers among research fields because our results indicate that papers tend to be cited in each research field locally. Therefore, one must consider the typology of targeted research areas when building models for link prediction in citation networks.