Using the alluvial plot to visualize the network characteristics of 100 top-cited articles on attention-deficit/hyperactivity disorder (ADHD) since 2011: Bibliometric analysis

Abstract

Background: Attention-deficit/hyperactivity disorder (ADHD) is a common neuro developmental disorder that affects children and adolescents. It is estimated that the prevalence of ADHD is 7.2% throughout the world. There have been a number of articles published in the literature related to ADHD. However, it remains unclear which countries, journals, subject categories, and articles have the greatest influence. The purpose of this study was to display influential entities in 100 top-cited ADHD-related articles (T100ADHD) on an alluvial plot and apply alluvial to better understand the network characteristics of T100ADHD across entities. Methods: Using the PubMed and Web of Science (WoS) databases, T100ADHD data since 2011 were downloaded. The dominant entities were compared using alluvial plots based on citation analysis. Based on medical subject headings (MeSH terms) and research areas extracted from PubMed and WoS, social network analysis (SNA) was performed to classify subject categories. To examine the difference in article citations among subject categories and the predictive power of MeSH terms on article citations in T100ADHD, one-way analysis of variance and regression analysis were used. Results: The top 3 countries (the United States, the United Kingdom, and the Netherlands) accounted for 75% of T100ADHD. The most citations per article were earned by Brazil (=415.33). The overall impact factor (IF = citations per 100) of the T100ADHD series is 188.24. The most cited article was written by Polanczyk et al from Brazil, with 772 citations since 2014. The majority of the articles were published and cited in Biol Psychiatry (13%; IF = 174.15). The SNA was used to categorize 6 subject areas. On the alluvial plots, T100ADHD’s network characteristics were successfully displayed. There was no difference in article citations among subject categories (F = 1.19, P = .320). The most frequently occurring MeSH terms were physiopathology, diagnosis, and epidemiology. A significant correlation was observed between MeSH terms and the number of article citations (F = 25.36; P < .001). Conclusion: Drawing the alluvial plot to display network characteristics in T100ADHD was a breakthrough. Article subject categories can be classified using MeSH terms to predict T100ADHD citations. Bibliometric analyses of 100 top-cited articles can be conducted in the future.

Using the Alluvial diagram to display variable characteristics for COVID-19 patients and research achievements on the topic of COVID-19, epidemiology, pathogenesis, and vaccine (CEPV): Bibliometric analysis

“…[37] Due to this, the Alluvial is more difficult to draw than the Sankey, especially under 3 circumstances: weights are calculated using SNA and allocated proportionately to nodes and arcs (or edges); the flows between dimensions are extracted backward from the SNA [23–25] rather than forward using the step-by-step process employed in the alluvial; and the nodes in the Alluvial diagram may be placed more freely than in a Sankey diagram, whose nodes must be aligned and cannot be placed randomly. [1]…”

“…[13][14][15] Since the parallel categorical dimensions lie on the x-axis, the Sankey in this case should be called Alluvial. [1] Variable characteristics for COVID-19 patients and research achievements (RAs) on the topic of COVID-19, epidemiology, pathogenesis, and vaccine (CEPV) can be illustrated by using the Alluvial diagrams.…”

“…[13–15] Since the parallel categorical dimensions lie on the x-axis, the Sankey in this case should be called Alluvial. [1]…”

“…Alluvial diagrams [1] were first used in the 2010 paper Mapping Change in Large Networks [2] by Martin Rosvall and Carl T. Bergstrom, in which they used this type of visualization to illustrate the changes in a large and complex network structure over time. Specifically, the citation patterns between approximately 7000 scientific journals over a decade were used to map the changes across a set of scientific fields using Alluvial.…”

Trend and prediction of citations on the topic of neuromuscular junctions in 100 top-cited articles since 2001 using a temporal bar graph: A bibliometric analysis

“…The top five most cited entities were selected by performing SNA via Equation (7) using the data in T100NMJ. [ 27 ] The relatively high density (i.e., the weighted citations in Equation (7)) was used to represent the dominant role in the respective cluster using the Alluvial plot [ 34 ] to display(see how to draw the Alluvial in Supplemental Digital Content 1, http://links.lww.com/MD/H365 ). Only citations for dominant entities were considered for the following analyses (e.g., trend and prediction).…”