Generalization of bibliographic coupling and co-citation using the node split network |
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| Institution: | 1. Hasselt University, Belgium;2. KU Leuven, Belgium, University of Antwerp, Belgium;1. Kyoto University Japan;2. RIKEN AIP Japan;1. Warsaw University of Technology, Faculty of Mathematics and Information Science, ul. Koszykowa 75, Warsaw 00-662, Poland;2. Deakin University, School of IT, Geelong, VIC 3220, Australia;3. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, Warsaw 01-447, Poland;4. Warsaw University of Technology, Faculty of Physics, ul. Koszykowa 75, Warsaw 00-662, Poland;1. Department of Statistics, Cochin University of Science and Technology, Cochin 682022, India;2. Department of Statistics, Government Arts College, Thiruvananthapuram 695014, India;1. Department of Library and Information Science Education, College of Education, Kongju National University, Gongju 32588, Republic of Korea;2. Department of Library and Information Science, Hannam University, Daejeon 34430, Republic of Korea;3. Department of Library and Information Science, Cheongju University, Cheongju 28503, Republic of Korea;4. Department of Library and Information Science, Yonsei University, Seoul 03722, Republic of Korea |
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| Abstract: | Bibliographic coupling (BC) and co-citation (CC) are the two most common citation-based coupling measures of similarity between scientific items. One can interpret these measures as second-neighbor relations distinguished by the direction of the citation: BC is a similarity between two citing items, whereas CC is that between two cited items. A previous study proposed a two-layer node split network that can emulate clusters of coupling measures in a computationally efficient manner; however, the lack of intralayer links makes it impossible to obtain exact similarities. Here, we propose novel methods to estimate intralayer similarity on a node split network using personalized PageRank (PPR) and neural embedding (EMB). We demonstrate that PPR is strongly correlated with the coupling measures. Moreover, our proposed method can yield precise similarities between items even if they are distant from each other. We also show that many links with high similarity are missing in the original BC/CC network, which suggests that it is essential to consider long-range similarities. Comparative experiments on global and local edge sampling suggest that local sampling is stable for PPR in node split networks. This analysis offers valuable insights into the process of searching for significantly related items regarding each coupling measure. |
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