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Temporal bibliographic networks
Institution:1. National Research University Higher School of Economics, Myasnitskaya, 20, 101000 Moscow, Russia;2. Institute of Mathematics, Physics and Mechanics, Jadranska 19, 1000 Ljubljana, Slovenia;3. University of Primorska, Andrej Maru?i? Institute, 6000 Koper, Slovenia;1. National Research University Higher School of Economics, Myasnitskaya, 20, 101000 Moscow, Russia;2. Institute of Mathematics, Physics and Mechanics, Jadranska 19, 1000 Ljubljana, Slovenia;3. University of Primorska, Andrej Maru?i? Institute, 6000 Koper, Slovenia;1. International Joint Informatics Laboratory (IJIL), Nanjing University, Nanjing, 210023, China;2. International Joint Informatics Laboratory (IJIL), University of Illinois, Champaign, United States;3. Jiangsu Key Laboratory of Data Engineering and Knowledge Service, School of Information Management, Nanjing University, Nanjing, 210023, China;1. Library, Nanjing Medical University, Nanjing, 210029, China;2. State Key Laboratory of Analytical Chemistry for Life Science, School of Electronic Science and Engineering, Nanjing University, Nanjing, 210023, China;1. Laboratory for Studies of Research and Technology Transfer, Institute for System Analysis and Computer Science (IASI-CNR), National Research Council of Italy, Viale Manzoni 30, 00185 Rome, Italy;2. Department of Engineering and Management, University of Rome “Tor Vergata”, Via del Politecnico 1, 00133 Rome, Italy;1. Sapienza University of Rome, Italy;2. SCImago Group, Madrid, Spain;3. SCImago Group, Dept. Information and Communication, University of Extremadura, Badajoz, Spain;4. University Complutense of Madrid, Information Science Faculty, Dept. Information and Library Science, SCImago Group, Spain;1. Center for Modern Korean Studies, Yonsei University, Wonju, Republic of Korea;2. Department of Library and Information Science, Yonsei University, Seoul, Republic of Korea;3. College of Computing and Informatics, Drexel University, Philadelphia, USA
Abstract:We present two ways (instantaneous and cumulative) to transform bibliographic networks, using the works’ publication year, into corresponding temporal networks based on temporal quantities. We also show how to use the addition of temporal quantities to define interesting temporal properties of nodes, links and their groups thus providing an insight into evolution of bibliographic networks. Using the multiplication of temporal networks we obtain different derived temporal networks providing us with new views on studied networks. The proposed approach is illustrated with examples from the collection of bibliographic networks on peer review.
Keywords:Social network analysis  Temporal networks  Linked networks  Bibliographic networks  Temporal quantities  Semiring  Network multiplication  Fractional approach
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