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Predicting High Impact Academic Papers Using Citation Network Features

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Trends and Applications in Knowledge Discovery and Data Mining (PAKDD 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7867))

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Abstract

Predicting future high impact academic papers is of benefit to a range of stakeholders, including governments, universities, academics, and investors. Being able to predict ‘the next big thing’ allows the allocation of resources to fields where these rapid developments are occurring. This paper develops a new method for predicting a paper’s future impact using features of the paper’s neighbourhood in the citation network, including measures of interdisciplinarity. Predictors of high impact papers include high early citation counts of the paper, high citation counts by the paper, citations of and by highly cited papers, and interdisciplinary citations of the paper and of papers that cite it. The Scopus database, consisting of over 24 million publication records from 1996-2010 across a wide range of disciplines, is used to motivate and evaluate the methods presented.

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Author information

Authors and Affiliations

  1. Research School of Computer Science, The Australian National University, Canberra, Australia

    Daniel McNamara, Peter Christen & Kee Siong Ng

  2. Office of Research Excellence, The Australian National University, Canberra, Australia

    Paul Wong

  3. EMC Greenplum, Singapore

    Kee Siong Ng

Authors
  1. Daniel McNamara
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  2. Paul Wong
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  3. Peter Christen
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  4. Kee Siong Ng
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Editor information

Editors and Affiliations

  1. School of Information Technology and Mathematical Sciences, University of South Australia, 1 Mawson Lakes Boulevard, 5095, Adelaide, SA, Australia

    Jiuyong Li

  2. Advanced Analytics Institute, University of Technology, 2-12 Blackfriars Street, Chippendale, Blackfriars Campus, 2008, Sydney, NSW, Australia

    Longbing Cao  & Can Wang  & 

  3. Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, 117576, Singapore, Singapore

    Kay Chen Tan

  4. School of Automation, Guangdong University of Technology, No. 100 Waihuan Xi Road, Panyu District, 510006, Guangzhou, China

    Bo Liu

  5. School of Computing Science, Simon Fraser University, 8888 University Drive, V5A 1S6, Burnaby, BC, Canada

    Jian Pei

  6. Department of Computer Science and Information Engineering, National Cheng Kung University, No.1, University Road, 701, Tainan, Taiwan

    Vincent S. Tseng

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© 2013 Springer-Verlag Berlin Heidelberg

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McNamara, D., Wong, P., Christen, P., Ng, K.S. (2013). Predicting High Impact Academic Papers Using Citation Network Features. In: Li, J., et al. Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7867. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40319-4_2

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  • DOI: https://doi.org/10.1007/978-3-642-40319-4_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40318-7

  • Online ISBN: 978-3-642-40319-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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