AI 中文总结
研究人员针对大规模引用图的多维度科学影响力计算需求，开发了基于Spark的开源软件库BIP! Ranker，可处理含数十亿次引用的网络，解决了现有开源方案不足的问题。
AI 中文摘要
科学影响力是多维度的：整体影响力、当前热度、早期引用动量以及领域相对表现，分别体现了出版物影响力的不同方面。但实际应用中，这些维度常被简化为单一指标，如引用次数。大规模计算多种互补影响指标的开源方案仍较匮乏，尤其是针对大型学术数据库提供的引用图。本文介绍BIP! Ranker，这是一个基于Spark的开源库，用于大规模计算基于引用的影响指标，能够处理数亿篇出版物间数十亿次引用的引用网络。
英文摘要
Scientific impact is multidimensional: overall influence, current popularity, early citation momentum, and field-relative performance each capture a distinct facet of a publication's impact. Yet, in practice, these dimensions are often reduced to a single metric, such as citation count. Open solutions for computing multiple complementary impact indicators at scale remain scarce, particularly for citation graphs as large as those provided by major scholarly databases. We introduce BIP! Ranker, an open-source, Spark-based library for computing citation-based impact indicators at scale, capable of processing citation networks with billions of citations among hundreds of millions of publications.
