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. 2023 Nov 3;102(44):e34801.
doi: 10.1097/MD.0000000000034801.

A modern approach with follower-leading clustering algorithm for visualizing author collaborations and article themes in skin cancer research: A bibliometric analysis

Po-Chih Lai  1 , Willy Chou  2   3 , Tsair-Wei Chien  4 , Feng-Jie Lai  5
Affiliations

A modern approach with follower-leading clustering algorithm for visualizing author collaborations and article themes in skin cancer research: A bibliometric analysis

Po-Chih Lai et al. Medicine (Baltimore). .

Abstract

Background: Skin cancers (SCs) arise due to the proliferation of atypical cells that have the potential to infiltrate or metastasize to different areas of the body. There is a lack of understanding regarding the country-based collaborations among authors (CBCA) and article themes on SCs. A clustering algorithm capable of categorizing CBCA and article themes on skin cancer is required. This study aimed to apply a follower-leading clustering algorithm to classify CBCA and article themes and present articles that deserve reading in recent ten years.

Methods: Between 2013 and 2022, a total of 6526 articles focusing on SC were extracted from the Web of Science core collection. The descriptive, diagnostic, predictive, and prescriptive analytics model was employed to visualize the study results. Various visualizations, including 4-quadrant radar plots, line charts, scatter plots, network charts, chord diagrams, and impact beam plots, were utilized. The category, journal, authorship, and L-index score were employed to assess individual research achievements. Diagnostic analytics were used to cluster the CBCA and identify common article themes. Keyword weights were utilized to predict article citations, and noteworthy articles were highlighted in prescriptive analytics based on the 100 most highly cited articles on SC (T100SC).

Results: The primary entities contributing to SC research include the United States, the University of California, San Francisco in US, dermatology department, and the author Andreas Stang from Germany, who possess higher category, journal, authorship, and L-index scores. The Journal of the American Academy of Dermatology has published the highest number of articles (n = 336, accounting for 5.16% of the total). From the T100SC, 7 distinct themes were identified, with melanoma being the predominant theme (92% representation). A strong correlation was observed between the number of article citations and the keyword weights (F = 81.63; P < .0001). Two articles with the highest citation counts were recommended for reading.

Conclusion: By applying the descriptive, diagnostic, predictive, and prescriptive analytics model, 2 noteworthy articles were identified and highlighted on an impact beam plot. These articles are considered deserving of attention and could potentially inspire further research in the field of bibliometrics, focusing on relevant topics related to melanoma.

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Conflict of interest statement

The authors have no funding and conflicts of interest to disclose.

Figures

Figure 1.
Figure 1.
How the follower-leading clustering algorithm (FLCA) algorithm work using 4 panels to explain it.
Figure 2.
Figure 2.
Research achievements of 4 article entities based on the category, journal, authorship, and L-index (CJAL) score (n = 6526).
Figure 3.
Figure 3.
Trend analysis of articles on skin cancer since 2013 (n = 6526).
Figure 4.
Figure 4.
Prestigious journals on skin cancer since 2013 (n = 6526).
Figure 5.
Figure 5.
Comparison of visualizations with network charts using the traditional (top) and the follower-leading clustering algorithm (FLCA) (bottom) approaches (n = 6526).
Figure 6.
Figure 6.
Validation of follower-leading clustering algorithm (FLCA) algorithm with an example of country-based author collaboration (n = 6526).
Figure 7.
Figure 7.
Coword analysis of keywords plus (n = 6526) to cluster article themes.
Figure 8.
Figure 8.
Themes assigned to T100SC using theme analysis and chord diagrams.
Figure 9.
Figure 9.
Themes assigned to countries and their clusters based on T100SC.
Figure 10.
Figure 10.
Prediction of article citation using the keyword weights.
Figure 11.
Figure 11.
T100CA on an impact beam plot (IBP) (note. articles appear immediately on PubMed once the bubble of interest is clicked).
See this image and copyright information in PMC

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