Social Network Analysis and Visualization of Big Data Research Adoption: A Scientometrics Approach

Clicks: 3
ID: 312738
2025
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #516 of 705 articles by views in Journal of Computing & Biomedical Informatics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 705 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
The research focuses on analyzing big data research papers to advance the theoretical understanding of big data and development planning in enterprises and management. Five thousand one hundred forty-eight (5148) research papers published between 2014 and 2024 were gathered from the “Web of Science Core Collection” database. In our proposed study we use two social network analysis and visualization tools, namely CiteSpace and VOSviewer to extract and present data, including knowledge graphs illustrating authors, journals, publication growth, institutions, countries, and keyword clusters. Significant collaborations and citations among governments, institutions, and authors were identified through scientometric analysis. The USA, China, and the Czech Republic were identified as the leading countries with the most published papers. The study emphasizes the importance of visually analyzing emerging trends, structural changes, and research hotspots in big data research using scientometrics. By uncovering keyword co-occurrence networks, prominent authors, key research themes, breakthrough publications, and research development over time, the study proposes a research agenda for further exploration and deeper insights into big data. The study has limitations that can be overcome using different dataset websites and other scientometric tools, as mentioned in the conclusion and future work.
Reference Key
imported_1777056382_69ebba7e1cfd2 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Salman Qadri
Journal Journal of Computing & Biomedical Informatics
Year 2025
DOI
DOI not found
URL
https://jcbi.org/index.php/Main/article/view/1006
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.