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| Citation Prediction Based on Sustained Attention Decay |
| ZHANG Meiping, SHANG Mingsheng
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| Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 611731, China |
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Abstract Existing methods have problems such as low accuracy or time consuming. Through empirical analysis of real data sets, we find that there is a strong correlation between future citations and annual citation of papers, i.e. the sustained attention; meanwhile, the future citations have apparent characteristics of time decay. Thus we put forward a method based on sustained attention decay to predict the future citations of papers, and then to find papers of potential importance with these results. Experimental results on two benchmark data sets show that the proposed method can predict precisely with lower time complexity.
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Received: 17 March 2014
Published: 22 June 2026
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