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.

翻译：科学学（SciSci）是一门新兴学科，它利用大规模数据集，以科学方法研究科学自身的结构与演化。学术成果数字化数据的日益丰富为探索科学学提供了前所未有的机遇。在科学进步中，先前发现的知识主要激发新的科学思想，而引用行为能较好地反映科学研究的这种累积特性。选择具有潜在影响力参考文献的研究将在新兴出版物中占据先机。尽管同行评审是预测论文未来影响力的主要可靠途径，但基于引用记录预见持久影响力的能力，在大数据时代的科学影响力分析中愈发重要。本文提出一种用于长期科学影响力预测的注意力机制，并基于真实大规模引用数据集验证了该方法。研究结果突破了传统思维：与精确模拟原始幂律分布相比，强调有限注意力能更好地站在巨人的肩膀上。