Constructing Author Closeness Networks Using Scopus Bibliometric Data

Leo Migdal

-Dec 5, 2025, 5:52 PM

constructing author closeness networks using scopus bibliometric data

PyblioNet is a software tool for the creation, visualization and analysis of bibliometric networks based on Pybliometrics, NetworkX and VisJs. It combines a Python-based data collection tool that accesses the Scopus database with a browser-based visualization and analysis tool. It allows users to create networks of publication data based on citations, co-citations, shared authors, bibliographic coupling, and shared keywords. The first component is a python based data collection tool which downloads publication data from the Scopus database via Pybliometrics. Initial Scopus search is done by the user via advanced search query strings using the scopus search api. Based on this initial publication data, further information on cited and citing research are collected which e.g.

allows for computing bibliographic coupling and co-citation relationships for the initial publication data (using the scopus Abstract Retrieval API and scopus Search API). The publication data is then used to create a network where each publication is represented as a node in the network. Relationships are computed based on shared authors, citation analysis, bibliographic coupling, co-citation analysis, and shared keywords: The second component is a html / JavaScript analysis and visualisation tool building on the VisJs Package. In the network each node represents a publication. Each edge represents a relation between two publications.

Nodes’ positions are calculated via a force-directed or hierarchical layout algorithm. The analysis tool allows for filtering, and graphical analysis. Filtering can be done based on publication date, degree centrality or weight etc. Graphical analysis covers e.g. searching and highlighting nodes based on user input, community detection based on a louvain cluster detection method etc. Users can use PyblioNet by executing a Python file, which requires the installation of the libraries such as Pybliometrics, NetworkX, etc.

Alternatively, users can run the exe file, which includes all necessary libraries. For the first use, users need to enter a valid Scopus API key in order to access the database via Pybliometrics (see also here). After that, users can start by entering Scopus advanced search query strings. PyblioNet will display how many publications were found using the search query and ask the user if they want to continue. If so, the user can continue with a standard setting, or with an advanced mode where the user can decide on the following settings: Find items in UIC Library collections, including books, articles, databases and more.

Find items on the UIC Library website, including research guides, help articles, events and website pages. CRExplorer. Cited References Explorer uses data downloaded from Scopus and Web of Science to perform citation analysis over time and is often used to determine influential publications in a subject area. Free to download and use. Publish or Perish. A software program that pulls information from several databases, including Web of Science, Scopus, Google Scholar, Microsoft Academic, and CrossRef.

Free to download and use. ScientoPyUI. An open-source software program allows users to import data downloaded from Scopus and Web of Science to perform a scientific analysis of citations, find the H-Index, and more. Free to download and use. Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2363)) Included in the following conference series:

Understanding patterns of closeness and collaboration between authors in contemporary bibliometric research is important for shaping the intellectual structure of the scientific field. Traditional methods such as co-authorship networks provide valuable insights but there are limitations. In this article, we examine a method for constructing author closeness networks, enhancing the approach based on bibliometric coupling networks. We explore a metric that measures the closeness of two authors based on the number of authors cited by both authors. We construct traditional co-authoring networks and networks based on the closeness criteria. We examine differences in network structure based on their giant components, and also conduct an analysis of their maximal cliques and dense communities.

The results show that these two approaches complement each other, allowing us to identify both the features of author interactions and the structure of authors engaged in related research. Supported by the Russian Science Foundation, project 22-18-00153. This is a preview of subscription content, log in via an institution to check access. Networks can provide significant measures to identify data driven patterns and dependencies. Though, given a data file it can be difficult to discern how one may approach creating such a network. In this tutorial, we will use a bibliographic data file downloaded from a query search in Scopus to walk through the process of cleaning the data file, writing a python script to parse the...

We tried out multiple Python libraries for ease of use and efficiency before landing on this combination. Building a network was more intuitive in NetworkX than iGraph. However, it took several minutes to render our large graph and a interaction was sticky. Pyvis was easy to build a network with and can be expanded to incorporate more advanced NetworkX functionality with only a couply lines of code. However it still took a long time to render, with slow manipulation. Holoviews, which runs on top of the native Python visualization library Bokeh, enables NetworkX to render quickly, with versitile manipulation.

The graphs are produced in HTML and JavaScript for easy integration into webpages. While we originally developed this script in a local notebook, we found that running it through Google's cloud-based Jupyter notebook environment Colaboratory is a smoother option, particularly for nacent coders. We encountered version conflicts between the dependencies when setting up a local notebook environment that were bipassed in Colab. Colaboratory allows you to use and share Jupyter notebooks from your browser, without having to download, install, or run anything on your own computer. Notebooks can be saved to Google Drive, Github or downloaded locally. This code contains OAuth2 functionality to access data from Google Drive, with a link to instructions for access from Github.

A single line of code adapts the script render in Colab. To open the notebook in Colab, click on the notebook from the repository list. GitHub will open a preview, click this icon from the top of the notebook to open directly in Colaboratory. (If the preview doesn't load, you may have to disable your ad blocker.) Alternatively, you can clone or download this repository and put in Google Drive. Google Drive will recognize the .ipynb notebook file format and give you the option to open in Colaboratory. The Colab file and the Jupyter Notebook file with an example csv can be found in their respective folders in the Github Repository.

To run the Jupyter Notebook, it would be best to clone the repositiory and open Jupyter Notebook from your local environment.

Constructing Author Closeness Networks Using Scopus Bibliometric Data

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