Similarity network fusion for scholarly journals

TL;DR

This study combines multiple network layers to analyze scholarly journal similarities, revealing the significant role of editors and the structure of research communities across different fields.

Contribution

It introduces an unsupervised network fusion method to integrate co-citations, common authors, and editors, highlighting the influence of editors in scholarly community boundaries.

Findings

Editors are the most influential layer in the fused network.

Clusters reflect sub-field specializations in sciences.

Economics clusters relate to methodological approaches.

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. Thus, the graphs representing the clusters of journals in the fused network are powerful instruments for exploring research fields.