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International Conference on Complex Networks and their Applications

COMPLEX NETWORKS 2017: Complex Networks & Their Applications VI pp 437–449Cite as

Network Signatures of Success: Emulating Expert and Crowd Assessment in Science, Art, and Technology

Part of the Studies in Computational Intelligence book series (SCI,volume 689)

Abstract

The success of scientific, artistic, and technological works is typically judged by human experts and the public. Recent empirical literature suggests that exceptionally creative works might have distinct patterns of citation. Given the recent availability of large citation and reference networks, we investigate how highly successful works differ from less successful ones in terms of a broad selection of centrality indices. Our experiments show that expert opinion is better emulated than popular judgment even with a single well-chosen index. Our findings further provide insights into otherwise implicit assumptions about indicators of success by evaluating the success of works based on the patterns of references that they receive.

Keywords

  • Crowd Assessment
  • Centrality Indices
  • IMDb Movies
  • Python Package Index (PyPI)
  • Internet Movie Database (IMDb)

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Notes

  1. 1.

    https://github.com/inguar/network-signatures-of-success-data.

  2. 2.

    https://api.mendeley.com/apidocs/docs.

  3. 3.

    http://www.imdb.com/interfaces.

  4. 4.

    https://www.loc.gov/programs/national-film-preservation-board/film-registry/.

  5. 5.

    https://libraries.io/data.

  6. 6.

    https://docs.continuum.io/anaconda/packages/pkg-docs.

  7. 7.

    https://developer.github.com/.

  8. 8.

    Authors of scientific papers can reference their forthcoming work or papers in the same volume. Movies can have anomalous references due to delayed release dates and avid marketing strategies in pre-release stage. Developers can freely change dependencies after publishing their packages, sometimes choosing more recent packages.

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Acknowledgements

Authors would like to thank Roberta Sinatra for valuable discussion and data on Nobel prize winners. Special thanks go to Noshir Contractor and SONIC lab members for supporting the preparation of this manuscript.I.Z. has been partially funded through the Russian academic excellence project “5–100”.

Author information

Authors and Affiliations

  1. Northwestern University, Evanston, IL, USA

    Igor Zakhlebin & Emőke-Ágnes Horvát

  2. NRU Higher School of Economics, Moscow, Russia

    Igor Zakhlebin

Authors
  1. Igor Zakhlebin
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  2. Emőke-Ágnes Horvát
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Corresponding author

Correspondence to Igor Zakhlebin .

Editor information

Editors and Affiliations

  1. University of Lyon 2, Lyon, France

    Chantal Cherifi

  2. University of Burgundy, Dijon, France

    Hocine Cherifi

  3. École Normale Supérieure de Lyon, Lyon, France

    Márton Karsai

  4. University College London, London, United Kingdom

    Mirco Musolesi

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Zakhlebin, I., Horvát, EÁ. (2018). Network Signatures of Success: Emulating Expert and Crowd Assessment in Science, Art, and Technology. In: Cherifi, C., Cherifi, H., Karsai, M., Musolesi, M. (eds) Complex Networks & Their Applications VI. COMPLEX NETWORKS 2017. Studies in Computational Intelligence, vol 689. Springer, Cham. https://doi.org/10.1007/978-3-319-72150-7_36

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  • DOI: https://doi.org/10.1007/978-3-319-72150-7_36

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