Time-aware PageRank for bibliographic networks |
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| Authors: | Dalibor Fiala |
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| Institution: | 1. Department of Information Management, Peking University, Beijing 100871, China;2. Information Retrieval and Knowledge Mining Laboratory, School of Information Management, Wuhan University, Wuhan, Hubei 430072, China;3. Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China;4. Department of Information Management, Peking University, Beijing 100871, China;5. Information Retrieval and Knowledge Mining Laboratory, School of Information Management, Wuhan University, Wuhan, Hubei 430072, China |
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| Abstract: | In the past, recursive algorithms, such as PageRank originally conceived for the Web, have been successfully used to rank nodes in the citation networks of papers, authors, or journals. They have proved to determine prestige and not popularity, unlike citation counts. However, bibliographic networks, in contrast to the Web, have some specific features that enable the assigning of different weights to citations, thus adding more information to the process of finding prominence. For example, a citation between two authors may be weighed according to whether and when those two authors collaborated with each other, which is information that can be found in the co-authorship network. In this study, we define a couple of PageRank modifications that weigh citations between authors differently based on the information from the co-authorship graph. In addition, we put emphasis on the time of publications and citations. We test our algorithms on the Web of Science data of computer science journal articles and determine the most prominent computer scientists in the 10-year period of 1996–2005. Besides a correlation analysis, we also compare our rankings to the lists of ACM A. M. Turing Award and ACM SIGMOD E. F. Codd Innovations Award winners and find the new time-aware methods to outperform standard PageRank and its time-unaware weighted variants. |
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