Similarity network fusion for scholarly journals

Federica Baccini, Lucio Barabesi, Alberto Baccini, Mahdi Khelfaoui and Yves Gingras

Journal of Informetrics, 2022, vol. 16, issue 1

Abstract: This paper explores intellectual and social proximity among scholarly journals by using network fusion techniques. Similarities among journals are initially represented by means of a three-layer network based on co-citations, common authors and common editors. The information contained in the three layers is then combined by building a fused similarity network. The fusion consists in an unsupervised process that exploits the structural properties of the layers. Subsequently, partial distance correlations are adopted for measuring the contribution of each layer to the structure of the fused network. Finally, the community morphology of the fused network is explored by using modularity. In the three fields considered (i.e. economics, information and library sciences and statistics) the major contribution to the structure of the fused network arises from editors. This result suggests that the role of editors as gatekeepers of journals is the most relevant in defining the boundaries of scholarly communities. In information and library sciences and statistics, the clusters of journals reflect sub-field specializations. In economics, clusters of journals appear to be better interpreted in terms of alternative methodological approaches.

Keywords: Similarity network fusion; Generalized distance correlation; Partial distance correlation; Multilayer social networks; Communities in networks; Co-citation network; Interlocking authorship network; Interlocking editorship network; Gatekeepers, (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:infome:v:16:y:2022:i:1:s1751157721000973

DOI: 10.1016/j.joi.2021.101226

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