Unsupervised prediction of citation influences

  • Authors:
  • Laura Dietz;Steffen Bickel;Tobias Scheffer

  • Affiliations:
  • Max Planck Institute for Computer Science, Saarbrücken, Germany;Max Planck Institute for Computer Science, Saarbrücken, Germany;Max Planck Institute for Computer Science, Saarbrücken, Germany

  • Venue:
  • Proceedings of the 24th international conference on Machine learning
  • Year:
  • 2007

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Abstract

Publication repositories contain an abundance of information about the evolution of scientific research areas. We address the problem of creating a visualization of a research area that describes the flow of topics between papers, quantifies the impact that papers have on each other, and helps to identify key contributions. To this end, we devise a probabilistic topic model that explains the generation of documents; the model incorporates the aspects of topical innovation and topical inheritance via citations. We evaluate the model's ability to predict the strength of influence of citations against manually rated citations.