Learning to Predict Citation-Based Impact Measures

@article{Weihs2017LearningTP,
  title={Learning to Predict Citation-Based Impact Measures},
  author={Luca Weihs and Oren Etzioni},
  journal={2017 ACM/IEEE Joint Conference on Digital Libraries (JCDL)},
  year={2017},
  pages={1-10}
}
  • Luca Weihs, Oren Etzioni
  • Published 1 June 2017
  • Computer Science
  • 2017 ACM/IEEE Joint Conference on Digital Libraries (JCDL)
Citations implicitly encode a community's judgment of a paper's importance and thus provide a unique signal by which to study scientific impact. Efforts in understanding and refining this signal are reflected in the probabilistic modeling of citation networks and the proliferation of citation-based impact measures such as Hirsch's h-index. While these efforts focus on understanding the past and present, they leave open the question of whether scientific impact can be predicted into the future… 

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