Predicting future influence of papers,researchers, and venues in a dynamic academic network |
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| Institution: | 1. School of Computer Science, Jiangsu University, China;2. School of Computing, Ulster University, UK;1. College of Liberal Arts and Sciences, National University of Defense Technology, Changsha, China;2. Department of Mathematics, University of California, Los Angeles, USA;1. National Research Council Canada, Ottawa, Ontario K1K 2E1 Canada;2. Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Montréal, Québec H3G 2W1 Canada;1. School of Information, Shanxi University of Finance & Economics, 030006, Taiyuan, China;2. CNKI, Inc., 100192, Beijing, China;1. College of Liberal Arts and Sciences, National University of Defense Technology, Changsha, China;2. Department of Mathematics, University of California, Los Angeles, USA |
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| Abstract: | Performance evaluation and prediction of academic achievements is an essential task for scientists, research organizations, research funding bodies, and government agencies alike. Recently, heterogeneous networks have been used to evaluate or predict performance of multi-entities including papers, researchers, and venues with some success. However, only a minimum of effort has been made to predict the future influence of papers, researchers and venues. In this paper, we propose a new framework WMR-Rank for this purpose. Based on the dynamic and heterogeneous network of multiple entities, we extract seven types of relations among them. The framework supports useful features including the refined granularity of relevant entities such as authors and venues, time awareness for published papers and their citations, differentiating the contribution of multiple coauthors to the same paper, amongst others. By leveraging all seven types of relations and fusing the rich information in a mutually reinforcing style, we are able to predict future influence of papers, authors and venues more precisely. Using the ACL dataset, our experimental results demonstrate that the proposed approach considerably outperforms state-of-the art competitors. |
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| Keywords: | Academic influence prediction Dynamic academic network Paper citation Mutual reinforcement |
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