  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.
