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.

翻译：文献计量分析是理解科学研究结构、动态与影响力的关键工具。传统方法往往难以捕捉科学文献中复杂的关联关系与演变趋势。为弥补这一不足，我们推出pyBiblioNet——一个专为促进全面网络化文献计量分析而设计的Python库，可深入解析引文网络、合著网络与关键词共现网络。该库与全球研究系统的开放目录OpenAlex集成，使用户能够便捷地对文献计量数据进行预处理、可视化与分析。核心功能包括：主题筛选、通过OpenAlex API自动下载数据、构建待分析的文献根集与基集、创建引文与合著网络、网络可视化工具，以及一套专为文献计量领域定制的网络中心性计算、聚类与社区发现算法。此外，该库支持运用自然语言处理技术分析关键主题与概念。我们以跨学科的"15分钟城市范式"为例开展文献计量分析，展示了该库在揭示不同科学领域中隐藏模式与新兴趋势方面的实用价值。pyBiblioNet将为研究人员、图书馆员与政策制定者提供强大易用的工具，助力提升文献计量分析水平并实现数据驱动的决策。