Fine-grained classification of journal articles based on multiple layers of information through similarity network fusion: The case of the Cambridge Journal of Economics

Alberto Baccini (), Federica Baccini, Lucio Barabesi, Martina Cioni, Eugenio Petrovich and Daria Pignalosa ()
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Alberto Baccini: Università degli Studi di Siena
Federica Baccini: Università degli Studi di Roma “La Sapienza”
Lucio Barabesi: Università degli Studi di Siena
Martina Cioni: Università degli Studi di Siena
Eugenio Petrovich: Università degli Studi di Torino

Scientometrics, 2024, vol. 129, issue 1, No 12, 373-400

Abstract: Abstract In order to explore the suitability of a fine-grained classification of journal articles by exploiting multiple sources of information, articles are organized in a two-layer multiplex. The first layer conveys similarities based on the full-text of articles, and the second similarities based on cited references. The information of the two layers are only weakly associated. The Similarity Network Fusion process is adopted to combine the two layers into a new single-layer network. A clustering algorithm is applied to the fused network and the classification of articles is obtained. In order to evaluate its coherence, this classification is compared with the ones obtained by applying the same algorithm to each of two layers. Moreover, the classification obtained for the fused network is also compared with the classifications obtained when the layers of information are integrated using different methods available in literature. In the case of the Cambridge Journal of Economics, Similarity Network Fusion appears to be the best option. Moreover, the achieved classification appears to be fine-grained enough to represent the extreme heterogeneity characterizing the contributions published in the journal.

Keywords: Similarity network fusion; Generalized distance correlation; Partial distance correlation; Multilayer social networks; Communities in networks; Topic modeling (search for similar items in EconPapers)
JEL-codes: A1 B2 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11192-023-04884-2

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