BIP! Ranker: A Software Library for Citation-Based Impact Indicators on Large-Scale Graphs
2026-08-03 • Digital Libraries
Digital LibrariesInformation Retrieval
AI summaryⓘ
The authors explain that scientific impact is complex and includes different parts like how widely a paper is used over time or how it compares to others in its field. Usually, people just look at one simple number, like citation counts, which misses these nuances. To help with this, the authors created BIP! Ranker, a free tool that can handle huge amounts of citation data to measure many different impact aspects all at once. This tool uses big data technology to work with very large citation networks efficiently.
scientific impactcitation countcitation networkimpact indicatorsSparkcitation momentumfield-relative performanceopen-source softwarebig data
Authors
Ilias Kanellos, Serafeim Chatzopoulos, Thanasis Vergoulis
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.