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A look at interdisciplinarity using bipartite scholar/journal networks

Author

Listed:
  • Chiara Carusi

    (University of Rome “Tor Vergata”)

  • Giuseppe Bianchi

    (University of Rome “Tor Vergata”)

Abstract

In this paper, we propose new means to quantify journals’ interdisciplinarity by exploiting the bipartite relation between scholars and journals where such scholars do publish. Our proposed approach is entirely data-driven (i.e., unsupervised): we just rely on the spectral properties of the bipartite bibliometric network, without requiring any a-priory classification or labeling of scholars or journals. Our approach is based on two subsequent steps. First, the structure of the bipartite graph is used to co-cluster both journals and scholars in a same low-dimensional space. Then, we measure a journal’s interdisciplinarity by computing various diversity metrics (Shannon entropy, Simpson diversity, Rao-Stirling index) over the journal’s distance with respect to these clusters. The proposed approach is evaluated over a dataset comprising 1258 journals and 2570 scholars in the information and communication technology field.

Suggested Citation

  • Chiara Carusi & Giuseppe Bianchi, 2020. "A look at interdisciplinarity using bipartite scholar/journal networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(2), pages 867-894, February.
    • Handle: RePEc:spr:scient:v:122:y:2020:i:2:d:10.1007_s11192-019-03309-3
    DOI: 10.1007/s11192-019-03309-3
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    References listed on IDEAS

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    3. Cantone, Giulio Giacomo, 2024. "How to measure interdisciplinary research? A systematic, yet critical, review," MetaArXiv hva4p, Center for Open Science.
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    5. Baccini, Federica & Barabesi, Lucio & Baccini, Alberto & Khelfaoui, Mahdi & Gingras, Yves, 2022. "Similarity network fusion for scholarly journals," Journal of Informetrics, Elsevier, vol. 16(1).

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