Similarity network fusion for scholarly journals |
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| Institution: | 1. Department of Computer Science, University of Pisa, and IIT-CNR of Pisa, Italy;2. Department of Economics and Statistics, University of Siena, Italy;3. Université du Québec à Trois-Riviéres, Canada;4. Université du Québec à Montreal, Canada;1. Department of Mathematics, Louisiana State University, Baton Rouge, LA, USA;2. Department of Mathematics and Statistics, Williams College, Williamstown, MA, USA;3. Graduate Program in Data Science, New College of Florida, Sarasota, FL, USA;4. Department of Experimental Statistics, Louisiana State University, Baton Rouge, LA, USA;1. Science Policy and Strategy Department Administrative Headquarters of the Max Planck Society, Hofgartenstr. 8, 80539 Munich, Germany;2. Ludwig-Maximilians-Universität Munich, Department of Sociology, Konradstr. 6, 80801 Munich, Germany;1. Department of Physics, University of Thessaloniki, and Center of Complex Systems, Thessaloniki 54124, Greece;2. Department of Physics, Bar-Ilan University, Ramat-Gan 52900, Israel;1. School of Information Management, Nanjing University, Nanjing, Jiangsu Province 210093, China;2. School of Journalism and Communication, Hunan University, Changsha, Hunan Province 410082, China;1. School of Information Management, Wuhan University, Wuhan 430072, China;2. Information Retrieval and Knowledge Mining Laboratory, Wuhan University, Wuhan 430072, China;3. Department of Information Management, Peking University, Beijing 100871, China |
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| 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. |
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