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A look at interdisciplinarity using bipartite scholar/journal networks

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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.

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Notes

  1. https://www.scopus.com/.

  2. The Cineca database is freely accessible at http://cercauniversita.cineca.it/php5/docenti/cerca.php.

  3. In Carusi and Bianchi (2019), as a final pre-processing step we discarded journals with papers published by less than five different scholars, and then we discarded scholars with less than five publications on this final set of journals. Before this last pre-processing step, in the present work we also excluded journals with less than 5 different publications authored by the scholars in the dataset.

  4. Please note that, having an entry for each individual scholar (in the ICT case, \(n=2570\)), \(\mathbf {a}_l\) is generally a high-dimensional and sparse vector.

  5. We are borrowing only the term “concept vectors” from Dhillon and Modha (2001), since we resort to the same formulation in terms of normalized centroids after K-Means clustering. It is important to make it clear that here concept vectors are not defined on the original, high-dimensional dataset, as in Dhillon and Modha (2001), but are referred to the reduced SVD space. Therefore, properties of concept vectors discussed in Dhillon and Modha (2001) do not apply to our case.

  6. With respect to the choice of the number of clusters, see discussion in Carusi and Bianchi (2019).

  7. In factor analysis, inspection of factor loadings in order is a common practice to help interpretation of results. Sidorova et al. (2008) provides a similar example for document-word analysis.

  8. The Simpson index is bounded between 0 and 1 by definition. In order to keep all diversity metrics within the same value range [0, 1], the logarithm base in Shannon entropy was set equal to the total number of communities K, whereas for the Rao-Stirling index, since there is no (finite) maximal possible distance d, we normalized all distances as a proportion of the maximum distance \(\max \limits _{i,j} d({\mathcal {C}}_i, {\mathcal {C}}_j)\).

  9. Even though not evident from the 5-community classification, lower-level communities emerge when we increase the number of co-clusters up to 6, 7, 8 and 9 communities, addressing in order research on propagation&antennas, pattern recognition, bioinformatics and pervasive computing. We refer the reader to Carusi and Bianchi (2019) for the full discussion on the results obtained with different choices of the input K of the co-clustering algorithm.

  10. The SCImago Journal Rank is publicly available at https://www.scimagojr.com/journalrank.php.

  11. Out of a total number of 1258 journals comprised in the ICT dataset, there were 133 journals with no SJR score retrieved from the SCImago database, mainly due to differences in the title wording.

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Authors and Affiliations

  1. Electronic Engineering Department, University of Rome “Tor Vergata”, Rome, Italy

    Chiara Carusi & Giuseppe Bianchi

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  1. Chiara Carusi
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  2. Giuseppe Bianchi
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Correspondence to Chiara Carusi.

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Carusi, C., Bianchi, G. A look at interdisciplinarity using bipartite scholar/journal networks. Scientometrics 122, 867–894 (2020). https://doi.org/10.1007/s11192-019-03309-3

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