Attention: to Better Stand on the Shoulders of Giants

• URL: http://arxiv.org/abs/2005.14256v1
• Date: Wed, 27 May 2020 00:25:51 GMT
• Title: Attention: to Better Stand on the Shoulders of Giants
• Authors: Sha Yuan, Zhou Shao, Yu Zhang, Xingxing Wei, Tong Xiao, Yifan Wang, Jie Tang
• Abstract summary: This paper develops an attention mechanism for the long-term scientific impact prediction. It validates the method based on a real large-scale citation data set.
• Score: 34.5017808610466
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Science of science (SciSci) is an emerging discipline wherein science is used to study the structure and evolution of science itself using large data sets. The increasing availability of digital data on scholarly outcomes offers unprecedented opportunities to explore SciSci. In the progress of science, the previously discovered knowledge principally inspires new scientific ideas, and citation is a reasonably good reflection of this cumulative nature of scientific research. The researches that choose potentially influential references will have a lead over the emerging publications. Although the peer review process is the mainly reliable way of predicting a paper's future impact, the ability to foresee the lasting impact based on citation records is increasingly essential in the scientific impact analysis in the era of big data. This paper develops an attention mechanism for the long-term scientific impact prediction and validates the method based on a real large-scale citation data set. The results break conventional thinking. Instead of accurately simulating the original power-law distribution, emphasizing the limited attention can better stand on the shoulders of giants.

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