SankeyNetwork: A clear and concise visualization tool for bibliometric data

This study proposes a novel framework to overcome the limitations of traditional bibliometric visualizations-such as co-word network charts-by integrating Sankey diagrams with author collaborations and co-word occurrences to better identify key contributors and themes. Analyzing 2252 articles published in the Journal of METHODSX (2020-2024), the study focuses on ten essential metadata elements commonly used in bibliometric evaluations, including country, institution, department, authorship, and keywords. Three complementary approaches are introduced: (1) a summarized performance sheet to present key metrics across entities, (2) Sankey diagrams for streamlined cluster visualization using the Following-Leading Clustering Algorithm (FLCA), and (3) slope graphs to track temporal trends and research bursts. Findings highlight the dominance of the United States, Symbiosis International in India, and author Fengxiang X Han, with the keyword "MODEL" emerging as most frequent. A 2020 article by Wondimagegn Mengist received the highest citation count (370). Slope graphs showed upward trends in four core elements over the past four years. The study concludes that these methods provide clearer insights while reducing visual complexity, and recommends combining performance sheets, Sankey diagrams, and slope graphs in future bibliometric analyses to better detect hotspots and evolving research patterns.•Sankey diagrams to enhance traditional bibliometric visualization methods.•Analyzing 2252 articles from Journal of METHODSX (2020-2024) to highlight author collaborations.•Key insights include the prominence of U.S., and Symbiosis International (India) in author collaborations.