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Predicting the Evolution of Scientific Output

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Computational Collective Intelligence (ICCCI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10448))

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Abstract

Various efforts have been made to quantify scientific impact and identify the mechanisms that influence its future evolution. The first step is the identification of what constitutes scholarly impact and how it is measured. In this direction, various approaches focus on future citation count or h-index prediction at author or publication level, on fitting the distribution of citation accumulation or accurately identifying award winners, upcoming hot research topics or academic rising stars. A plethora of features have been contemplated as possible influential factors and assorted machine-learning methodologies have been adopted to ensure timely and accurate estimations. Here, we provide an overview of the field challenges, as well as a taxonomy of the existing approaches to identify the open issues that are yet to be addressed.

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Author information

Authors and Affiliations

  1. Department of Informatics, Aristotle University, 54124, Thessaloniki, Greece

    Antonia Gogoglou & Yannis Manolopoulos

Authors
  1. Antonia Gogoglou
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  2. Yannis Manolopoulos
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Corresponding author

Correspondence to Antonia Gogoglou .

Editor information

Editors and Affiliations

  1. Department of Information Systems, Faculty of Computer Science and Management , Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Department of Computer Science, University of Cyprus, Nicosia, Cyprus

    George A. Papadopoulos

  3. Department of Information Systems, Gdynia Maritime University, Gdynia, Poland

    Piotr Jędrzejowicz

  4. Department of Information Systems, Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

  5. Department of Information Systems, University of Münster, Münster, Germany

    Gottfried Vossen

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Gogoglou, A., Manolopoulos, Y. (2017). Predicting the Evolution of Scientific Output. In: Nguyen, N., Papadopoulos, G., Jędrzejowicz, P., Trawiński, B., Vossen, G. (eds) Computational Collective Intelligence. ICCCI 2017. Lecture Notes in Computer Science(), vol 10448. Springer, Cham. https://doi.org/10.1007/978-3-319-67074-4_24

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  • DOI: https://doi.org/10.1007/978-3-319-67074-4_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67073-7

  • Online ISBN: 978-3-319-67074-4

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