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The effect of data pre-processing on understanding the evolution of collaboration networks

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  • Kim, Jinseok
  • Diesner, Jana

Abstract

This paper shows empirically how the choice of certain data pre-processing methods for disambiguating author names affects our understanding of the structure and evolution of co-publication networks. Thirty years of publication records from 125 Information Systems journals were obtained from DBLP. Author names in the data were pre-processed via algorithmic disambiguation. We applied the commonly used all-initials and first-initial based disambiguation methods to the data, generated over-time networks with a yearly resolution, and calculated standard network metrics on these graphs. Our results show that initial-based methods underestimate the number of unique authors, average distance, and clustering coefficient, while overestimating the number of edges, average degree, and ratios of the largest components. These self-reinforcing growth and shrinkage mechanisms amplify over time. This can lead to false findings about fundamental network characteristics such as topology and reasoning about underlying social processes. It can also cause erroneous predictions of trends in future network evolution and suggest unjustified policies, interventions and funding decisions. The findings from this study suggest that scholars need to be more attentive to data pre-processing when analyzing or reusing bibliometric data.

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  • Kim, Jinseok & Diesner, Jana, 2015. "The effect of data pre-processing on understanding the evolution of collaboration networks," Journal of Informetrics, Elsevier, vol. 9(1), pages 226-236.
    • Handle: RePEc:eee:infome:v:9:y:2015:i:1:p:226-236
    DOI: 10.1016/j.joi.2015.01.002
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    1. Milojević, Staša, 2013. "Accuracy of simple, initials-based methods for author name disambiguation," Journal of Informetrics, Elsevier, vol. 7(4), pages 767-773.
    2. Ricardo G. Cota & Anderson A. Ferreira & Cristiano Nascimento & Marcos André Gonçalves & Alberto H. F. Laender, 2010. "An unsupervised heuristic-based hierarchical method for name disambiguation in bibliographic citations," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(9), pages 1853-1870, September.
    3. Fuyuki Yoshikane & Kyo Kageura, 2004. "Comparative analysis of coauthorship networks of different domains: The growth and change of networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 60(3), pages 435-446, August.
    4. Brent D Fegley & Vetle I Torvik, 2013. "Has Large-Scale Named-Entity Network Analysis Been Resting on a Flawed Assumption?," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-16, July.
    5. Dongwook Shin & Taehwan Kim & Joongmin Choi & Jungsun Kim, 2014. "Author name disambiguation using a graph model with node splitting and merging based on bibliographic information," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(1), pages 15-50, July.
    6. Abramo, Giovanni & D’Angelo, Ciriaco Andrea & Murgia, Gianluca, 2013. "The collaboration behaviors of scientists in Italy: A field level analysis," Journal of Informetrics, Elsevier, vol. 7(2), pages 442-454.
    7. Fiala, Dalibor, 2012. "Time-aware PageRank for bibliographic networks," Journal of Informetrics, Elsevier, vol. 6(3), pages 370-388.
    8. Wagner, Caroline S. & Leydesdorff, Loet, 2005. "Network structure, self-organization, and the growth of international collaboration in science," Research Policy, Elsevier, vol. 34(10), pages 1608-1618, December.
    9. Andreas Strotmann & Dangzhi Zhao, 2012. "Author name disambiguation: What difference does it make in author-based citation analysis?," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(9), pages 1820-1833, September.
    10. Perc, Matjaž, 2010. "Growth and structure of Slovenia’s scientific collaboration network," Journal of Informetrics, Elsevier, vol. 4(4), pages 475-482.
    11. Andreas Strotmann & Dangzhi Zhao, 2012. "Author name disambiguation: What difference does it make in author‐based citation analysis?," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(9), pages 1820-1833, September.
    12. Barabási, A.L & Jeong, H & Néda, Z & Ravasz, E & Schubert, A & Vicsek, T, 2002. "Evolution of the social network of scientific collaborations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 311(3), pages 590-614.
    13. Massimo Franceschet, 2011. "Collaboration in computer science: A network science approach," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(10), pages 1992-2012, October.
    14. José María Cavero & Belén Vela & Paloma Cáceres, 2014. "Computer science research: more production, less productivity," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(3), pages 2103-2111, March.
    15. Massimo Franceschet, 2011. "Collaboration in computer science: A network science approach," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(10), pages 1992-2012, October.
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    1. Jinseok Kim & Liang Tao & Seok-Hyoung Lee & Jana Diesner, 2016. "Evolution and structure of scientific co-publishing network in Korea between 1948–2011," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(1), pages 27-41, April.
    2. Jinseok Kim, 2019. "A fast and integrative algorithm for clustering performance evaluation in author name disambiguation," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(2), pages 661-681, August.
    3. Jinseok Kim & Jana Diesner, 2019. "Formational bounds of link prediction in collaboration networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(2), pages 687-706, May.
    4. Janaína Gomide & Hugo Kling & Daniel Figueiredo, 2017. "Name usage pattern in the synonym ambiguity problem in bibliographic data," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(2), pages 747-766, August.
    5. Jinseok Kim & Jenna Kim & Jason Owen‐Smith, 2021. "Ethnicity‐based name partitioning for author name disambiguation using supervised machine learning," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(8), pages 979-994, August.
    6. Jinseok Kim, 2018. "Evaluating author name disambiguation for digital libraries: a case of DBLP," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(3), pages 1867-1886, September.
    7. Šubelj, Lovro & Fiala, Dalibor & Ciglarič, Tadej & Kronegger, Luka, 2019. "Convexity in scientific collaboration networks," Journal of Informetrics, Elsevier, vol. 13(1), pages 10-31.

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