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

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.

Automated summary

By utilizing multiple sources of information and applying similarity network fusion, a fine-grained classification of journal articles is achieved. The results show that similarity network fusion is the best option in some cases and the classification obtained accurately represents the heterogeneity of the journal articles.