Predicting the popularity of scientific publications has attracted much attention from various disciplines. In this paper, we focus on the popularity prediction problem of scientific papers, and propose an age-based diffusion (AD) model to identify papers that will receive more citations and be popular in the near future. The AD model mimics the attention diffusion process along the citation networks. An experimental study shows that the AD model can achieve better prediction accuracy than other benchmark methods. For some newly published papers that have not accumulated many citations but will be popular in the near future, the AD model can substantially improve their rankings. This improvement is critical, because identifying future highly cited papers from large numbers of new papers published each month would provide very valuable references for researchers.

预测科学出版物的流行度引起了各个学科的广泛关注。在本文中，我们关注科学论文的流行度预测问题，并提出了基于年龄的扩散（AD）模型来识别将获得更多引用并在不久的将来流行的论文。AD 模型模拟了沿引文网络的注意力扩散过程。一项实验研究表明，与其他基准方法相比，AD 模型可以实现更好的预测精度。对于一些新发表的论文，引用次数不是很多，但近期会很火，AD模型可以大幅提升他们的排名。这种改进很关键，