Visual impact beam plots: Analyzing research profiles and bibliometric metrics using the following-leading clustering algorithm (FLCA)

Abstract

Background: A new approach to showcasing author publications on a website involves using a visual representation instead of the conventional paper list. The creation of an impact beam plot (IBP) as a research profile for individuals is crucial, especially when incorporating collection edges that include self-cited articles through a rare cluster analysis technique not commonly found in the literature. This study presents the application of a unique method called the following-leading clustering algorithm (FLCA) to generate IBPs for 3 highly productive authors. Methods: For the 3 highly productive authors, Sung-Ho Jang from South Korea, Chia-Hung Kao from Taiwan, and Chin-Hsiao Tseng from Taiwan, all their published articles indexed in the Web of Science Core Collection were downloaded. Sung-Ho Jang published 593 articles, Chia-Hung Kao published 732 articles, and Chin-Hsiao Tseng published 160 articles. To analyze and showcase their publications, the FLCA was utilized. This algorithm helped cluster their articles and identify representative publications for each author. To assess the effectiveness and validity of the FLCA algorithm, both network charts and heatmaps with dendrograms were employed. IBPs were then created and compared for each of the 3 authors, taking into consideration their h-index, x-index, and self-citation rate. This allowed for a comprehensive visual representation of their research impact and citation patterns. Results: The results show that these authors’ h-index, x-index, and self-citation rates were (37, 44.01, 1.66%), (42, 61.47, 0.23%), and (37, 40.3, 6.62%), respectively. A higher value in these metrics indicates a more remarkable research achievement. A higher self-citation rate with a lower cluster number indicates that manuscripts are more likely to have been self-drafted. Using the FLCA algorithm, IBPs were successfully generated for each author. Conclusion: The FLCA algorithm allows for the easy generation of visual IBPs based on authors’ publication profiles. These IBPs incorporate 3 important bibliometric metrics: h-index, x-index, and self-citations. These metrics are highly recommended for use by researchers globally, particularly with the self-citation rate, as they offer valuable insights into the scholarly impact and citation patterns of individual researchers.

Visualizing burst spots on research for four authors in MDPI journals named to be Citation Laureates 2021 using temporal bar graph

“…[59] Fourth, the research accomplishments of individual authors can be effectively visualized using the Impact Beam Plot (IBP), [60] as has been demonstrated in previous literature. [61] Unfortunately, a limitation of our current study is that we have not employed IBPs to showcase the research profiles of the 4 distinguished authors. [58,[62][63][64] This could have provided a clearer picture of each author's contributions and the breadth and impact of their research work.…”

Trends and hotspots related to traditional and modern approaches on acupuncture for stroke: A bibliometric and visualization analysis

“…The FLCA algorithm of a 1-mode network can be expressed mathematically in Equations 1 and 2. [64][65][66] …”

“…To enhance comprehension of the clustering process based on the 2-mode keywords using FLCA, [64][65][66] 3 visualizations were employed to interpret the FLCA algorithm: a heatmap with a dendrogram, a circle bar plot, [75,76] and a network chart referred to the software Pajek [in Koeln; PajekMan in Osoje (Ossiach, Austria)]. [77] 2.3.3.…”

“…Figures 7 to 9 display the outcomes of cluster analysis employing FLCA algorithm. [64][65][66] The 10 clusters with keywords are presented in all 3 visualizations: the heatmap (Fig. 6), where the cluster leaders are located at the top with vertical followers shown on the right-hand side; the circle bar plot with observed counts; and the network chart depicting trends and hotspots over the years.…”

“…Med., Medicine (Baltimore), and Acupuncture in Medicine. Leveraging the FLCA algorithm, [64][65][66][67] our study delved into co-word and author collaboration analyses. Additionally, we outlined the prevailing research landscape, prospective trends, and spotlighted 3 standout articles through bibliometric evaluation.…”