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Analyzing the Strength of Co-authorship Ties with Neighborhood Overlap

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Database and Expert Systems Applications (Globe 2015, DEXA 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9261))

Abstract

Evaluating researchers’ scientific productivity usually relies on bibliometry only, which may not be always fair. Here, we take a step forward on analyzing such data by exploring the strength of co-authorship ties in social networks. Specifically, we build co-authorship social networks by extracting the datasets of three research areas (sociology, medicine and computer science) from a real digital library and analyze how topological properties relate to the strength of ties. Our results show that different topological properties explain variations in the strength of co-authorship ties, depending on the research area. Also, we show that neighborhood overlap can be applied to scientific productivity evaluation and analysis beyond bibliometry.

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Notes

  1. 1.

    Datasets available at http://www.dcc.ufmg.br/~mirella/Tools/DEXA2015/.

  2. 2.

    Lattes: http://lattes.cnpq.br.

  3. 3.

    ECDF assigns a probability of 1 / n to each value of neighborhood overlap and edge weight, sorts the data in increasing order, and calculates the sum of the assigned probabilities up to and including each value.

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Acknowledgments

The authors thank CAPES, CNPq and Fapemig - Brazil.

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Authors and Affiliations

  1. Universidade Federal de Minas Gerais, Belo Horizonte, Brazil

    Michele A. Brandão & Mirella M. Moro

Authors
  1. Michele A. Brandão
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  2. Mirella M. Moro
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Corresponding author

Correspondence to Mirella M. Moro .

Editor information

Editors and Affiliations

  1. Hewlett-Packard Enterprise, Sunnyvale, California, USA

    Qiming Chen

  2. Paul Sabatier University, Toulouse, France

    Abdelkader Hameurlain

  3. Blaise Pascal University, Aubiere, France

    Farouk Toumani

  4. University of Linz, Linz, Austria

    Roland Wagner

  5. Universidad Politécnica de Valencia, Valencia, Spain

    Hendrik Decker

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Brandão, M.A., Moro, M.M. (2015). Analyzing the Strength of Co-authorship Ties with Neighborhood Overlap. In: Chen, Q., Hameurlain, A., Toumani, F., Wagner, R., Decker, H. (eds) Database and Expert Systems Applications. Globe DEXA 2015 2015. Lecture Notes in Computer Science(), vol 9261. Springer, Cham. https://doi.org/10.1007/978-3-319-22849-5_37

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  • DOI: https://doi.org/10.1007/978-3-319-22849-5_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22848-8

  • Online ISBN: 978-3-319-22849-5

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