BIP! Ranker: A Software Library for Citation-Based Impact Indicators on Large-Scale Graphs

2026-08-03

AI Summary

Current approaches to research impact assessment predominantly rely on single metrics such as citation counts, which fail to comprehensively capture multidimensional aspects of scholarly influence—including overall impact, temporal dynamics, early momentum, and field-normalized performance. Moreover, there is a notable absence of open-source tools capable of supporting large-scale citation graph computations. To address these limitations, this work proposes an open-source software library built on Apache Spark that leverages optimized graph algorithms and distributed computing to enable efficient, scalable parallel computation of multidimensional influence metrics across academic graphs comprising tens of billions of publications and hundreds of billions of citations. This framework overcomes the scalability and dimensionality constraints of existing methods, substantially enhancing the feasibility, flexibility, and practicality of large-scale research evaluation.

Abstract

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.

Problem

• scientific impact
• citation-based indicators
• large-scale graphs
• multidimensional impact
• scholarly databases

Innovation

• citation-based impact indicators
• large-scale graphs
• Spark-based library
• scientific impact assessment
• open-source