BIP! Ranker: A Software Library for Citation-Based Impact Indicators on Large-Scale Graphs
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.
Create a lesson
Related papers
Shifting Research Funding Priorities under Geopolitical Pressure: Evidence from Estonia
Yunfeng Gao, Yang Ding
Geospatial Metadata Improves Discoverability by Connecting Datasets Across Scientific Disciplines
Daniel Ebanks, Devika Jain
Quantifying the impact of clinical-academic collaborations
Mohamad Zeina, Nick McNally, Karl S. Peggs et al.
Testing Our Foundations: Citation Trends, Errors, and Emerging Hallucinations in the Computing Education Literature
Paul Denny, Gweneth Barbre, Musa Blake et al.
Toward non-textual representation of social anthropology: Modeling cultures as knowledge graphs
Manolis Peponakis, Sarantos Kapidakis, Martin Doerr et al.
Geometric Signatures of Conceptual Reorganization: A Counterfactual Embedding Framework for Detecting Scientific Revolutions
Dimitris Ntounis, Ariel Schwartzman, Chris Chafe et al.