PyBiblioNet
A Python library for a comprehensive network-based bibliometric analysis
Bibliographic Data
| ID | 21442533 |
|---|---|
| Authors | Mirko Lai (0000-0001-5199-6950, Università degli Studi del Piemonte Orientale “Amedeo Avogadro”, corresponding author), Salvatore Vilella (0000-0002-7666-0318, Università degli Studi del Piemonte Orientale “Amedeo Avogadro”), Federica Cena (0000-0003-3481-3360, University of Turin), Giancarlo Ruffo (0000-0002-3407-9234, Università degli Studi del Piemonte Orientale “Amedeo Avogadro”) |
| Year | 2025 |
| Volume | 130 |
| Issue | 12 |
| Pages | 7139-7190 |
| Publication date | 2025-12-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Scientometrics (JOURNAL) |
| Journal identifiers | ISSN: 0138-9130 • E-ISSN: 1588-2861 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s11192-025-05458-0 |
| OpenAlex | W7118867968 |
| Language | EN |
| Citations received | 1 |
| References cited | 72 |
Bibliometric analysis is a critical tool for understanding the structure, dynamics, and impact of scientific research. Traditional methods often fall short in capturing the intricate relationships and evolving trends within scientific literature. To address this gap, we present pyBiblioNet, a Python library designed to facilitate comprehensive network-based bibliometric analysis, providing insights into citation networks, co-authorship networks, and keyword co-occurrence networks. The library integrates with OpenAlex, a popular and open catalogue to the global research system, enabling users to easily preprocess, visualize, and analyse bibliometric data. Key features include topic selection, automatic data download via OpenAlex APIs, creation of the root and base sets of manuscripts to analyze, creation of the citation and co-authorship networks, network visualization tools, and a suite of algorithms for computing network centralities, clustering, and community detection, all of them tailored to the bibliometric domain. Additionally, it enables the analysis of key topics and concepts using NLP techniques. We showcase the main functions of the library by performing a bibliometric analysis on the multidisciplinary “15-minute city paradigm”, demonstrating the utility of pyBiblioNet in uncovering hidden patterns and emerging trends in various scientific domains. pyBiblioNet can empower researchers, librarians, and policymakers with a powerful, user-friendly tool for enhancing their bibliometric analyses and making data-driven decisions
Bibliometrics · Citation · Citation analysis · Key (lock) · Multidisciplinary approach · Python (programming language) · Suite · Visualization · Bioinformatics and Genomic Networks · Complex Network Analysis Techniques · scientometrics and bibliometrics research
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Systematic analysis of generative AI tools integration in academic research and peer review
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Moving the 15-minute city beyond the urban core
The 15-minute city for all? – Measuring individual and temporal variations in walking accessibility
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On big data, artificial intelligence and smart cities
| Citations per year | 1 |
|---|---|
| Citation span | 2026 - 2026 (1) |
| Highly cited | No |