This paper explores the possibility of classifying journal articles by exploiting multiple information sources, instead of relying on only one information source at a time. In particular, the Similarity Network Fusion (SNF) technique is used to merge the different layers of information about articles when they are organized as a multiplex network. The method proposed is tested on a case study consisting of the articles published in the Cambridge Journal of Economics. The information about articles is organized in a two-layer multiplex where the first layer contains similarities among articles based on the full-text of articles, and the second layer contains similarities based on the cited references. The unsupervised similarity network fusion process combines the two layers by building a new single-layer network. Distance correlation and partial distance correlation indexes are then used for estimating the contribution of each layer of information to the determination of the structure of the fused network. A clustering algorithm is lastly applied to the fused network for obtaining a classification of articles. The classification obtained through SNF has been evaluated from an expert point of view, by inspecting whether it can be interpreted and labelled with reference to research programs and methodologies adopted in economics. Moreover, the classification obtained in the fused network is compared with the two classifications obtained when cited references and contents are considered separately. Overall, the classification obtained on the fused network appears to be fine-grained enough to represent the extreme heterogeneity characterizing the contributions published in the Cambridge Journal of Economics.

翻译：本文探讨了利用多种信息源而非仅依赖单一信息源对期刊论文进行分类的可能性。具体而言，采用相似性网络融合（SNF）技术，将论文的多层信息整合为多级网络结构。该方法以《剑桥经济学杂志》发表的论文为案例进行验证。论文信息被组织为双层多级网络：第一层基于全文内容构建论文间相似性，第二层基于参考文献构建相似性。无监督的SNF过程通过构建新的单层网络融合这两层信息。随后利用距离相关性和偏距离相关系数估算每层信息对融合网络结构形成的贡献度。最后对融合网络应用聚类算法实现论文分类。从专家视角评估了SNF生成的分类结果，通过检验其能否参照经济学研究纲领与方法论进行解释和标注。此外，将融合网络所得分类与分别基于参考文献和内容单独生成的两种分类进行了比较。总体而言，融合网络生成的分类具有足够细粒度，能够有效表征《剑桥经济学杂志》所发表成果的极端异质性特征。