The constantly increasing rate at which scientific papers are published makes it difficult for researchers to identify papers that currently impact the research field of their interest. Hence, approaches to effectively identify papers of high impact have attracted great attention in the past. In this work, we present a method that seeks to rank papers based on their estimated short-term impact, as measured by the number of citations received in the near future. Similar to previous work, our method models a researcher as she explores the paper citation network. The key aspect is that we incorporate an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher's preference to read papers which received a lot of attention recently. A detailed experimental evaluation on four real citation datasets across disciplines, shows that our approach is more effective than previous work in ranking papers based on their short-term impact.

中文翻译：

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按短期科学影响对论文进行排名

科学论文发表的速度不断增加，使得研究人员很难确定目前影响他们感兴趣的研究领域的论文。因此，有效识别具有高影响力的论文的方法在过去引起了极大的关注。在这项工作中，我们提出了一种方法，该方法旨在根据估计的短期影响对论文进行排名，以在不久的将来收到的引用次数来衡量。与之前的工作类似，我们的方法模拟了研究论文引用网络的研究人员。关键是我们采用了一种基于注意力的机制，类似于限时版本的优先依恋，以明确捕捉研究人员对阅读最近受到很多关注的论文的偏好。