  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.

Social and Information Networks;Machine Learning;Machine Learning