Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2601.16990

pyBiblioNet: a Python library for a comprehensive network-based bibliometric analysis

Mirko Lai·Salvatore Vilella·Federica Cena·Giancarlo Ruffo
View original source ↗Read source document ↗Export metadata ↓

Abstract

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.

Explore related subjects

cs.DL

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Mirko Lai, Salvatore Vilella, Federica Cena, Giancarlo Ruffo. 2026-01-07. pyBiblioNet: a Python library for a comprehensive network-based bibliometric analysis. https://doi.org/10.1007/s11192-025-05458-0

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

The illusion of neutrality in metric-based research evaluation

Venue prestige and citation counts are two widely used, albeit imperfect, signals of research quality. When the two signals conflict, evaluators must decide how much weight to assign each. Yet, it remains unknown how researchers across disciplines trade off these two signals when evaluating research outcome. To fill this gap, we surveyed 869 researchers using paired choices between hypothetical departmental hiring rules that assigned different weights to venue prestige and citation counts, asking which would produce better science. Among 795 respondents whose choices were largely internally consistent, choices placed nearly equal aggregate weight on the two signals. This apparent balance concealed substantial individual heterogeneity: nearly two in five respondents occupied the most venue-heavy or citation-heavy intervals. Moreover, respondents on average chose more citation-heavy rules than they believed their departments used in hiring. By revealing the subjective judgments that arise when research indicators conflict, our findings reinforce calls for greater caution when quantitative indicators are used to evaluate research.

cs.DL↗
arXiv2026

Reading the Data Back: Enriching Variable-Level Metadata for Model-Data Consistency Checks

Purpose: Research data repositories often lack variable-level metadata. Model-choice screening, checking whether a reported model suits the values of its outcome variable, also requires summaries of those values and links to the analyses that use them. We ask how repositories can represent and acquire them as metadata with evidence and review histories. Methods: We propose a metadata application profile compatible with the Data Documentation Initiative Cross-Domain Integration (DDI-CDI) model, linking versioned variables and empirical profiles to reported analyses, estimators, and analysis roles. Extraction provenance and review decisions are recorded separately. We evaluate streaming data profiling, deterministic extraction from Stata and R code, and language-model extraction of analysis records from papers. Results: A worked export of one replication package illustrates the profile: it passes shape and interchange checks and answers four queries, one of which returns the analyses that raise no alert. Screening 4,868 replication datasets from six political-science journals links outcomes to profiled columns in 1,440 deposits and flags 965, including 14 of the 21 deposits with a hand-verified linear model on a count, proportion, binary, or ordinal outcome. AI-assisted adjudication yields 55% precision on a near-balanced sample of 119 count and proportion candidates; screening rules were developed on the same corpus. Conclusion: The profile makes analysis-variable relationships queryable while preserving evidence and review history; screening yields review candidates with context. Code, metadata, and measurements are released.

cs.DL↗
arXiv2026

From Code Archival to Knowledge Graph: Bridging Software Heritage, COAR Notify and Wikidata

Software is a first-class scientific object, yet validated links between source code and the scholarly record remain largely absent from the Linked Open Data (LOD) cloud, isolating archived artefacts from semantic discovery. This paper presents an end-to-end reconciliation pipeline that harvests, validates, and models publication-to-repository pairs from sources where the link between a paper and its source code is explicit and editorially verified: the software-centric journals JOSS, SoftwareX, and IPOL, together with the reproducibility reports of the SIGMOD Availability and Reproducibility Initiative (ARI). This yields a curated corpus of 4,397 $\langle$DOI, repository-URL$\rangle$ pairs. We design two distinct application profiles grounded in Wikidata classes (one for scholarly articles, one for software instances) aligned with the schema.org and CodeMeta vocabularies. This architectural separation enables rule-based reconciliation at two granularities: lightweight, inline publication references or standalone, first-class Wikidata software nodes equipped with SWHIDs, Software Heritage's content-addressed identifiers. A read-only lookup against Wikidata shows that only 82 of the harvested repositories were already modelled there; human-reviewed batches have since created 4{,}182 new software items cross-linked to their articles. We further show that payloads of the emerging COAR Notify protocol, an external effort we do not develop, map natively onto our input format, so the same backend could later serve a live enrichment stream. Our core contribution is a pair of application profiles that turn Wikidata into a connector between the scholarly record and archived source code; we openly release all code, application profiles, and harvested datasets.

cs.DL↗