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In order to explore the suitability of a fine-grained classification ofjournal articles by exploiting multiple sources of information, articles areorganized in a two-layer multiplex. The first layer conveys similarities basedon the full-text of articles, and the second similarities based on citedreferences. The information of the two layers are only weakly associated. TheSimilarity Network Fusion process is adopted to combine the two layers into anew single-layer network. A clustering algorithm is applied to the fusednetwork and the classification of articles is obtained. In order to evaluateits coherence, this classification is compared with the ones obtained byapplying the same algorithm to each of two layers. Moreover, the classificationobtained for the fused network is also compared with the classificationsobtained when the layers of information are integrated using different methodsavailable in literature. In the case of the Cambridge Journal of Economics,Similarity Network Fusion appears to be the best option. Moreover, the achievedclassification appears to be fine-grained enough to represent the extremeheterogeneity characterizing the contributions published in the journal.

scientometrics

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