Graph Visualization Research Articles

Analysis of research trends and hotspots in emergency department overcrowding: A bibliometric study based on VOSview and Scimago Graphica

Objective Analyze the research trends and hotspots in emergency department overcrowding derived from the Web of Science Core Collection database. Methods The Web of Science Core Collection database was utilized as the search data source for the bibliometric analysis, and the associated articles published from January 1, 1990, to October 1, 2023.The search was executed using the following formula: TS = (crowded OR overcrowd OR crowding OR overcrowding) AND TS = (Emergency department). VOSviewer, Scimago Graphicaand and additional tools were utilized for bibliometric analysis, and visual knowledge graphs were created. Results A total of 1869 articles were included in this study. The country with the largest number of publications is the United States. The primary research institution is the University of Toronto. Jesse M. Pines and his group at George Washington University have the greatest influence in the field of emergency department overcrowding research. Carlos A. Camargo is the author with the highest h-index in this field. High-frequency keywords include “length-of-stay”, “impact”, “mortality”, “triage”, “association”, “outcomes”, “time”, “management”, “access block”, and “quality”. The clustering graph reveals that all keywords fall into seven categories. Conclusion We recommend intensifying research on emergency department overcrowding in more developing countries. In the future, the application of emerging technologies in emergency medicine as well as the mental health of emergency patients and medical staff may become research hotspots in this field.

Modifikasi Permainan Bowling dalam Meningkatkan Kemanpuan Membilang pada Anak Tunagrahita Ringan

This research aims to improve the numeracy ability of mildly impaired students at SLB Negeri 1 Lubuk Sikaping through the modification of bowling games. This study uses a quantitative research type through an experimental approach in the form of Single Subject Research (SSR) and applies an A-B-A design. There are two variables in this study, namely the modification of bowling games as an independent variable and the ability to count as a bound variable. The subject of this study was a mentally retarded student with the initials D, male in class IV-C SLB Negeri 1 Lubuk Sikaping who was not yet able to count numbers up to 20 even though he was already in class IV. To overcome this problem, the researcher aims to improve the ability to count in students by continuing the ability of students who were previously only able to count 1 to 10 continued by counting 11 to 15 through modification of the bowling game. The results of the study showed the percentage of results in the Baseline condition (A1) with a percentage of 33%, 33%, 33%, 33%. Intervention Condition (B) with percentages of 40%, 40%, 67%, 73%, 87%, 93%, 93%, 93%. For Baseline condition (A2) with percentages of 87%, 93%, 93%, 93%. The final data that has been analyzed will be in the form of percentages and visual graphs, and it can be concluded that bowling game modification can improve the ability to count numbers in mentally retarded children

Advancements in phasor-based FLIM: multi-component analysis and lifetime probes in biological imaging.

Fluorescence lifetime imaging microscopy (FLIM) is a reliable method that achieves imaging by detecting fluorescence lifetimes within samples. Owing to its unique temporal characteristic, it can complement fluorescence intensity measurement. Technological and methodological advancements in FLIM have broadened its applications across various domains. The processing of fluorescence lifetime data is crucial for enhancing the speed and accuracy of imaging. Thus, various lifetime fitting algorithms have been developed to improve the imaging speed. The phasor analysis (PA) method is an approach for processing fluorescence lifetime data, capable of directly converting lifetime signals into visual graphics without fitting, which outperforms traditional approaches in speed. Furthermore, lifetime probes with distinct lifetimes are readily implemented for visualization and cluster analysis combined with PA, facilitating the prediction of specific biological states or functions. This review examines various lifetime probes employed in phasor-based FLIM and discusses their roles in the PA method. The methods for multi-component PA within complex biological environments were also described. Additionally, we focused on the advantages of the phasor vector rule and the unmixing of multi-component analysis based on PA. The integration of lifetime probes with phasor-based FLIM facilitates rapid and intuitive detection methods for analyzing complex biological environments.

Rethinking the impact of noisy labels in graph classification: A utility and privacy perspective

Graph neural networks (GNNs) based on message-passing mechanisms have achieved advanced results in graph classification tasks. However, their generalization performance degrades when noisy labels are present in the training data. Most existing noisy labeling approaches focus on the visual domain or graph node classification tasks and analyze the impact of noisy labels only from a utility perspective. Unlike existing work, in this paper, we measure the effects of noise labels on graph classification from data privacy and model utility perspectives. We find that noise labels degrade the model’s generalization performance and enhance the ability of membership inference attacks on graph data privacy. To this end, we propose the robust graph neural network (RGLC) approach with noisy labeled graph classification. Specifically, we first accurately filter the noisy samples by high-confidence samples and the first feature principal component vector of each class. Then, the robust principal component vectors and the model output under data augmentation are utilized to achieve noise label correction guided by dual spatial information. Finally, supervised graph contrastive learning is introduced to enhance the embedding quality of the model and protect the privacy of the training graph data. The utility and privacy of the proposed method are validated by comparing twelve different methods on eight real graph classification datasets. Compared with the state-of-the-art methods, the RGLC method achieves at most and at least 7.8% and 0.8% performance gain at 30% noisy labeling rate, respectively, and reduces the accuracy of privacy attacks to below 60%.

Optimizing Diabetes Data Insights Through Kmapper-Based Topological Networks: A Decision Analytics Approach for Predictive and Prescriptive Modeling

A highly effective technique for identifying and showing structures in high-dimensional datasets is topological data analysis. The Kmapper software creates overlaying clustering graphs and topological networks to facilitate the investigation of such information. The objective of the work was to visualize a dataset of diabetes patients that included information on blood pressure, glucose levels, and pregnancies using the Kmapper software. Afterward, it applied topological data analysis to see if any underlying structures or patterns could be established. The preprocessed dataset of diabetic patients was acquired via Kaggle. Kmapper was run with a difference of parameter settings, which includes 0.4 overlap, 15 hypercubes, and varying numbers of PCA components (1, 2, and 3). We investigated the generated graph visualizations. Although two PCA components were used, the topological graphs disclosed likely intriguing highlights such as three peaks. To understand these illustrated structures in the background of the diabetes data, additional investigation is mandatory. Also with every aspect considered, Kmapper worked well for using topological representations to contribute intuition into the high-dimensional dataset.

Me! Me! Me! Me! A study and comparison of ego network representations

From social networks to brain connectivity, ego networks are a simple yet powerful approach to visualizing parts of a larger graph, i.e. those related to a selected focal node — the so-called “ego”. While surveys and comparisons of general graph visualization approaches exist in the literature, we note (i) the many conflicting results of comparisons of adjacency matrices and node-link diagrams, thus motivating further study, as well as (ii) the absence of such systematic comparisons for ego networks specifically. In this paper, we propose the development of empirical recommendations for ego network visualization strategies. First, we survey the literature across application domains and collect examples of network visualizations to identify the most common visual encodings, namely straight-line, radial, and layered node-link diagrams, as well as adjacency matrices. These representations are then applied to a representative, intermediate-sized network and subsequently compared in a large-scale, crowd-sourced user study in a mixed-methods analysis setup to investigate their impact on both user experience and performance. Within the limits of this study, and contrary to previous comparative investigations of adjacency matrices and node-link diagrams (outside of ego networks specifically), participants performed systematically worse when using adjacency matrices than those using node-link diagrammatic representations. Similar to previous comparisons of different node-link diagrams, we do not detect any notable differences in participant performance between the three node-link diagrams. Lastly, our quantitative and qualitative results indicate that participants found adjacency matrices harder to learn, use, and understand than node-link diagrams. We conclude that in terms of both participant experience and performance, a layered node-link diagrammatic representation appears to be the most preferable for ego network visualization purposes.

Living High-Training Low on Mice Bone Parameters Analyzed through Complex Network Approach.

The aim of this study was to investigate the effect of 8 weeks of hypoxic exposition and physical training on healthy mice femur outcomes analyzed through conventional statistic and complex networks. The mice were divided into four groups, subjected to physical training (T; 40 min per day at 80% of critical velocity intensity) or not (N), exposed to hypoxic environment ("Living High-Training Low" model - LHTL; 18 h per day, FIO2=19.5%; Hyp) or not (Nor). The complex network analysis performed interactions among parameters using values of critical "r" of 0.5 by Pearson correlations to edges construction, with Fruchterman-Reingold layout adopted for graph visualization. Pondered Degree, Betweenness, and Eigenvector metrics were chosen as centrality metrics. Two-way ANOVA, t-test and Pearson correlation were used with P<0.05. Femur phosphorus of T-Hyp was higher than all other groups (P<0.05) and correlated with bone density (r=0.65; P=0.042), bone mineral density (r=0.67; P=0.034) and% of mineral material (r=0.66, P=0.038). Overall, the complex network demonstrated improvements in bone volume, % of mineral material, bone density, and bone mineral density for T-Hyp over other groups. Association of physical training and hypoxia improved bone quality for healthy mice.

Haihun Marquis Tomb in Western Han Dynasty: Social Value and Future Prospects - A Knowledge Graph Analysis with Citespace

This paper uses the CiteSpace knowledge graph visualization tool to analyze the research evolution path and trend of 583 academic papers in the CNKI database in the past ten years. The results show that the unique position of the tomb of the Haihun Marquis in the field of history and cultural heritage of the Han Dynasty, the mainstream of research in the field of Han Dynasty culture, artistic innovation and application, the evolution of cultural heritage research, and the interdisciplinary research of the deep integration of cultural heritage research and advanced technology in the future will provide strong support for promoting the cultural and museum industry and the recreation of traditional Chinese culture.

Dynamic signatures: a mathematical approach to analysis

Abstract This study evaluates mathematical tools (principal component analysis, dynamic time warping, and the Kolmogorov–Smirnov hypothesis test) to analyse global and local data from dynamic signatures to reduce subjectivity and increase the reproducibility of handwriting examination using a two-step approach. A data set composed of 1800 genuine signature samples, 870 simulated signatures, and 60 disguises (30 formally similar or ‘autosimulated’ and 30 random but different from usual) provided by 30 volunteers was collected. The first step involved global data analysis using principal component analysis and a hypothesis test performed for 62 global characteristics, and associations of these characteristics were analysed through calculations of multivariate distance followed by a hypothesis test. The second step involved the analysis of local characteristics including vertical and horizontal positions, speed, pressure gradient, acceleration, and jerk point-to-point, by using dynamic time warping followed by a hypothesis test. Optimization of sensitivity and specificity metrics of the hypothesis test was explored by varying its stringency and observing accuracy rates for the simulated and genuine groups. A P-value threshold of 1 × 10−10 was found to be optimal, making the test more restrictive and yielding accuracy rates of 96.7% for genuine global data and 88.9% for simulated data. The same cutoff value for local characteristics provided an average accuracy rate of 95.4% for genuine samples and 94.7% for simulated samples, demonstrating high accuracy for both simulated and genuine samples. However, the method did not offer reasonable accuracy rates for disguises, consistent with observations in traditional handwriting examination. Our approach provided satisfactory results for forensic examination use. The visualization of graphs and signatures and analysis of all identifying elements of handwriting by the examining expert are still essential. In future studies, we plan to perform blind tests to validate our approach and propose a rigorous methodology.

Learning Heterogeneous Relation Graph and Value Regularization Policy for Visual Navigation.

The goal of visual navigation is steering an agent to find a given target object with current observation. It is crucial to learn an informative visual representation and robust navigation policy in this task. Aiming to promote these two parts, we propose three complementary techniques, heterogeneous relation graph (HRG), a value regularized navigation policy (VRP), and gradient-based meta learning (ML). HRG integrates object relationships, including object semantic closeness and spatial directions, e.g., a knife is usually co-occurrence with bowl semantically or located at the left of the fork spatially. It improves visual representation learning. Both VRP and gradient-based ML improve robust navigation policy, regulating this process of the agent to escape from the deadlock states such as being stuck or looping. Specifically, gradient-based ML is a type of supervision method used in policy network training, which eliminates the gap between the seen and unseen environment distributions. In this process, VRP maximizes the transformation of the mutual information between visual observation and navigation policy, thus improving more informed navigation decisions. Our framework shows superior performance over the current state-of-the-art (SOTA) in terms of success rate and success weighted by length (SPL). Our HRG outperforms the Visual Genome knowledge graph on cross-scene generalization with ≈ 56% and ≈ 39% improvement on Hits@ 5* (proportion of correct entities ranked in top 5) and MRR * (mean reciprocal rank), respectively. Our code and HRG datasets will be made publicly available in the scientific community.

BiodiViz: Leveraging NER and RE for Automated Knowledge Graph Generation in Biodiversity Research

In biodiversity research, the integration of machine learning and data visualization is increasingly important for uncovering valuable insights from academic literature. This study introduces an innovative knowledge graph application, BiodiViz, designed to translate intricate text into intuitive visual representations, fostering a deeper comprehension of biodiversity relationships. BiodiViz uses the top-performing Named Entity Recognition (NER) and Relation Extraction (RE) models to automatically generate a comprehensive knowledge graph for biodiversity research. The NER model extracts and categorizes entities like organisms, phenomena, and habitats, while the RE model identifies relationships such as "have," "occur in," and "influence" from the BiodivNERE dataset (Abdelmageed et al. 2022). These entities and relationships are organized into nodes and edges within a graph. Researchers input text into BiodiViz, producing a visual knowledge graph that simplifies the analysis of complex biodiversity data, reducing manual effort and enhancing efficiency. Named Entity Recognition &amp; Relation Extraction BiodiViz leverages advanced Bidirectional Encoder Representations from Transformers (BERT)-based Large Language Models (LLMs) (Rogers et al. 2020), fine-tuned specifically for NER and RE tasks using the BiodivNERE dataset. The fine-tuning process involved various models, including BERT (Devlin et al. 2019), ELECTRA (Clark et al. 2020), and BiodivBERT (Abdelmageed et al. 2023). These models were evaluated for performance using the results of their F1-score as the main metric, which is the harmonic mean of precision (the proportion of true positive results among all positive predictions) and recall (the proportion of true positive results among all actual positives), with BiodivBERT achieving an F1-score of 77.16% for the NER task, while BERT excelled in the RE task with an F1-score of 81.28%. Rigorous hyperparameter optimization further enhanced the performance of BiodivBERT in the RE task by 3.38%. The BiodivNERE corpora by Abdelmageed et al. (2022) were used to fine-tune several models for NER and RE tasks in the biodiversity domain. The first corpus from the BiodivNERE corpora is BiodivNER, which is a gold standard dataset (manually labelled test corpora) for evaluating NER tasks. The fine-tuning process employed the token classification method from the Hugging Face library (Hugging Face 2023b), which assigns labels to each token in a sequence. Experiments were conducted with a batch size of four, meaning the model processes four examples/rows of data at a time before making an update to improve its learning. This is due to the constraints of the NVIDIA® GeForce RTX™ 3060 graphics processor. (NVIDIA 2024) Model performance was evaluated using the seqeval library (Nakayama 2018), focusing on accuracy, precision, recall, and F1 scores. For text classification, the second corpus, BiodivRE, was utilized, following previous research recommendations to explore fine-tuning settings for BiodivBERT. Hyperparameter optimization (Feurer and Hutter 2019) was conducted using Hugging Face’s Trainer API with an Optuna backend (Hugging Face 2023a), concentrating on learning rate and the number of training epochs (i.e., the number of complete passes through the entire dataset during model training). The BiodiViz Knowledge Graph Application The fine-tuned NER and RE models with the best F1-scores—BiodivBERT and BERT, respectively—were integrated into the knowledge graph application. Fig. 1 illustrates the flowchart of the application pipeline. Each sentence in the input text will go through the NER model to identify and label the entities within the sentence. Subsequently, these labeled entities, together with the original sentence, will be input into the RE model. The RE model will analyze every pair of entities for a potential relation and output the type of relation they share. The application will then utilize this data to create a graph with appropriate labels and color-coding. An example of the application's user interface with the knowledge graph is shown in Fig. 2. This study highlights the practical application of machine learning and data visualization in advancing biodiversity research, emphasizing the importance of developing user-friendly tools to support scientific exploration and discovery. The BiodiViz application, including the code and resources, is available on GitHub*1, providing an accessible tool for biodiversity researchers to streamline their analyses.

Computing Hive Plots: A Combinatorial Framework

Hive plots are a graph visualization style placing vertices on a set of radial axes emanating from a common center and drawing edges as smooth curves connecting their respective endpoints. In previous work on hive plots, assignment to an axis and vertex positions on each axis were determined based on selected vertex attributes and the order of axes was prespecified. Here, we present a new framework focusing on combinatorial aspects of these drawings to extend the original hive plot idea and optimize visual properties such as the total edge length and the number of edge crossings in the resulting hive plots. Our framework comprises three steps: (1) partition the vertices into multiple groups, each corresponding to an axis of the hive plot; (2) optimize the cyclic axis order to bring more strongly connected groups near each other; (3) optimize the vertex ordering on each axis to minimize edge crossings. Each of the three steps is related to a well-studied, but NP-complete computational problem. We combine and adapt suitable exact and heuristic algorithmic approaches, implement them as an instantiation of our framework, and show in a case study how it can be applied in a practical setting. Furthermore, we conduct computational experiments to gain further insights regarding the algorithmic choices of our framework. The code of the implementation and a prototype web application can be found on OSF.

Arguing with numbers in social media based COVID-19 debates: A study of the argumentative functions of visual graphs

This study engages with a growing body of literature on numerical communication in COVID-19, which suggests that numbers and numerical measurements can be manipulated to shape governmental policy and public health measures. It uniquely focuses on numerical graphs generated through professional open-resource visualization tools but used by laypersons in social media debates. By combining theories of multimodality and linguistic theories of argumentativity, we analyzed Twitter interactions on COVID-19 policy that occurred on the Twitter account of a leading public health figure in Scotland. Our fine-grained qualitative analysis shows that while language choices are essential for establishing the argumentative context for graphs, choices about how to encode visual information, such as the placement of elements in the composition, can also provide information about the communicator’s intention. To understand the argumentative purpose of the graphs, the interlocutor needs to grasp not only how information is packaged in the multimodal message but also the argumentative agenda of the speaker/writer, which is encoded in their multimodal message and other contextual information.

Sequence Flow: interactive web application for visualizing partial order alignments

BackgroundMultiple sequence alignment (MSA) has proven extremely useful in computational biology, especially in inferring evolutionary relationships via phylogenetic analysis and providing insight into protein structure and function. An alternative to the standard MSA model is partial order alignment (POA), in which aligned sequences are represented as paths in a graph rather than rows in a matrix. While the POA model has proven useful in several applications (e.g. sequencing reads assembly and pangenome structure exploration), we lack efficient visualization tools that could highlight its advantages.ResultsWe propose Sequence Flow – a web application designed to address the above problem. Sequence Flow presents the POA as a Sankey diagram, a kind of graph visualisation typically used for graphs representing flowcharts. Sequence Flow enables interactive alignment exploration, including fragment selection, highlighting a selected group of sequences, modification of the position of graph nodes, structure simplification etc. After adjustment, the visualization can be saved as a high-quality graphic file. Thanks to the use of SanKEY.js – a JavaScript library for creating Sankey diagrams, designed specifically to visualize POAs, Sequence Flow provides satisfactory performance even with large alignments.ConclusionsWe provide Sankey diagram-based POA visualization tools for both end users (Sequence Flow) and bioinformatic software developers (SanKEY.js). Sequence Flow webservice is available at https://sequenceflow.mimuw.edu.pl/. The source code for SanKEY.js is available at https://github.com/Krzysiekzd/SanKEY.js and for Sequence Flow at https://github.com/Krzysiekzd/SequenceFlow.

Research Status and Evolutionary Trends of Digitally Empowered Community Emergency Management: Knowledge Graph Analysis Based on CiteSpace

(1) Background: With the acceleration of globalization, various risks are intertwined and coupled, and community emergency management is facing greater pressure than before. The continuous development of digital technology has made it possible to empower community emergency management with digital technology. Therefore, it is urgent to have a clear understanding of the research status and future development direction of digital empowering community emergency management. (2) Methods: This study is based on the Web of Science database and utilizes the CiteSpace knowledge graph visualization method to analyze the research progress of community digital emergency management. (3) Results: The results indicate that from 2000 to 2024, the amount of research on community digital emergency management has been on the rise, involving multiple disciplines, but there has not been a close and mature cooperation network between different countries, institutions, and authors. The research topic has shifted from early single-emergency management to integration with emerging science and technology, with a greater emphasis on the role of communities. Big data, machine learning, technological innovation, digital healthcare, and community resilience are important research directions for the future. (4) Conclusions: In the future, we should strengthen cooperation in this field, overcome issues such as the digital divide and digital security, and promote further development of community digital emergency management.

Dual-level adaptive incongruity-enhanced model for multimodal sarcasm detection

Multimodal sarcasm detection leverages multimodal information, such as image, text, etc. to identify special instances whose superficial emotional expression is contrary to the actual emotion. Existing methods primarily focused on the incongruity between text and image information for sarcasm detection. Existing sarcasm methods in which the tendency of image encoders to encode similar images into similar vectors, and the introduction of noise in graph-level feature extraction due to negative correlations caused by the accumulation of GAT layers and the lack of representations for non-neighboring nodes. To address these limitations, we propose a Dual-Level Adaptive Incongruity-Enhanced Model (DAIE) to extract the incongruity between the text and image at both token and graph levels. At the token level, we bolster token-level contrastive learning with patch-based reconstructed image to capture common and specific features of images, thereby amplifying incongruities between text and images. At the graph level, we introduce adaptive graph contrast learning, coupled with negative pair similarity weights, to refine the feature representation of the model’s textual and visual graph nodes, while also enhancing the information exchange among neighboring nodes. We conduct experiments using a publicly available sarcasm detection dataset. The results demonstrate the effectiveness of our method, outperforming several state-of-the-art approaches by 3.33% and 4.34% on accuracy and F1 score, respectively.

Topology of quasi divisor graphs associated with non-associative algebra

The visualization of graphs representing algebraic structures has increasingly gained traction in chemical engineering research, emerging as a significant scientific challenge in contemporary studies. Due to their practical applications, graphs associated with groups and various algebraic systems have garnered significant interest in academic literature. Chemical graph theory employs graphs to illustrate molecular structures, where vertices symbolize atoms and edges represent bonds. The structure of quasi divisor graphs linked to non-associative algebra, including quasigroups and loops, presents numerous potential implications and wider applications. These applications span multiple disciplines, such as algebra, combinatorics, computer science, and network theory. Latin squares are tabular representations of quasigroups, generalizations of group structure. Numerous classifications of quasigroups exist in academic literature, such as Moufang quasigroups, Bol quasigroups, and alternative quasigroups. However, the structural characteristics of quasigroups exhibiting the weak inverse property closely resemble those of groups. The overall purpose of this study is to see the interrelationship between non-isomorphic quasi divisor graphs and a particular class of G-loops. To achieve our results, we employ a variety of methods, including a combinatorial approach, degree counting techniques, vertex and edge partitioning methods, as well as graph-theoretical tools and analytical procedures. In this paper, we compute a few topological indices based on degree, distance, and degree-distance using the structural aspects of quasi divisor graphs of orders 2φ,22ϕ,23ϕ and 4ϕ1ϕ2 where φ is a positive integer and ϕ1,ϕ2 are odd primes with ϕ1<ϕ2. This work also includes polynomial forms and their geometrical interpretations connected to linear, quadratic and higher degree topological indices of quasi divisor graphs associated with a class of non-commutative weak inverse property quasigroups.

Coconut Libtool: Bridging Textual Analysis Gaps for Non‐Programmers

ABSTRACTIn the era of big and ubiquitous data, professionals and students alike are finding themselves needing to perform a number of textual analysis tasks. Historically, the general lack of statistical expertise and programming skills has stopped many with humanities or social sciences backgrounds from performing and fully benefiting from such analyses. Thus, we introduce Coconut Libtool (www.coconut-libtool.com/), an open‐source, web‐based application that utilizes state‐of‐the‐art natural language processing (NLP) technologies. Coconut Libtool analyzes text data from customized files and bibliographic databases such as Web of Science, Scopus, and Lens. Users can verify which functions can be performed with the data they have. Coconut Libtool deploys multiple algorithmic NLP techniques at the backend, including topic modeling (LDA, Biterm, and BERTopic algorithms), network graph visualization, keyword lemmatization, and sunburst visualization. Coconut Libtool is the people‐first web application designed to be used by professionals, researchers, and students in the information sciences, digital humanities, and computational social sciences domains to promote transparency, reproducibility, accessibility, reciprocity, and responsibility in research practices.

Visual analysis of research hotspots and trends in traditional Chinese medicine for depression in the 21st century: a bibliometric study based on Citespace and VOSviewer

Background and ObjectivesDepression long been a key concern for scholars worldwide; however, the field of depression has not received sufficient attention in traditional Chinese medicine. It was not until the 21st century that research into depression gradually entered a period of rapid development, with an increasing number of academic studies published in major journals. However, one limitation of this field is that no scholars have yet summarised the development process and key research issues. Therefore, the present study aimed to summarise the research trends and progress in this field, providing relevant information and presenting potential future research directions for subsequent researchers. MethodsLiterature in this field was searched from 1 January 2000 to 20 April 2024 in the Web of Science Core Collection database, to analyse the current status of the literature and publication trends. Bibliographic information, including study authors, organisations, keywords, countries, references, citations, and co-citations, was extracted using CiteSpace and VOSviewer software for quantitative analysis, visual mapping, and scientific evaluation. ResultsA total of 921 papers were included, with a significant increase in the number of publications from 2017 to 2021, and a stable number of more than 140 publications between 2022 and 2023, with publications in these two years accounting for 31.38% of the total. The Journal of Ethnopharmacology had the highest number of publications (97) and citations (2067), as well as the highest number of co-citations (1369). China (847 publications, 13256 citations), Beijing University of Chinese Medicine (90 publications, 1232 citations), and Qin Xuemei (30 publications, 759 citations) were the most prolific and influential countries, organisations, and authors in the field, respectively. Keyword clustering co-occurrence analysis revealed nine different clusters with good homogeneity. The top three clusters were randomised controlled trials, traditional Chinese medicine, and hippocampal neurogenesis. In the timeline analysis of keywords, from 2000 to 2010, keywords in this field were concentrated on hippocampal neurology and forced swimming test as clustering axes of Traditional Chinese Medicine. From 2010 to 2020, the research hotspots focused on randomised controlled trials and hippocampal neurogenesis. After 2020, keywords became more focused on network pharmacology. In addition, the occurrence time of explosive keywords were distributed before 2010 and after 2020. Before 2010, these keywords included the forced swimming test, Tail Suspension Test, Chronic Cold Stress, Neural Regeneration, and Banxia Houpu Decoction. Conversely, network Pparmacology and Molecular Docking arose as key buzzwords starting in 2020. ConclusionsThis study comprehensively analysed and summarised the research hotspots and trends in this field of research in the 21st century from a bibliometric perspective, further generating a series of visual graphs to help researchers understand the current research status, potential collaborators, collaborating institutions, and potential future research hotspots in this field.

The Visual Rhetoric in Comics for Reader Interpretation and Engagement to Meaning-Making in the Medium

Comics are a unique form of sequential art that combines text and visual graphics to tell a story. In academic circles, the purposeful use of visual elements to convey meaning or visual rhetoric has been gaining increasing attention. This research explores how comics' use of visual rhetoric affects readers' interpretations of and interactions with the medium. Additionally, the study seeks to explore how the discussion about models and concepts used affects readers' interactions with the medium and their meaning-making processes. To achieve this, the research employed qualitative methods, using descriptive and semi-systematic approaches to gather information on visual rhetoric related to three predetermined objectives. The study's results advance the field of comics studies by offering new insights into the potential of visual rhetoric to convey complex ideas and abstract concepts. It also contributes to the academic study of comics by providing fresh perspectives on using visual rhetoric to convey complex ideas. Overall, this research offers a better understanding of the interplay between visual elements and textual components in comics, helping readers better appreciate the medium's storytelling capabilities. Through a detailed analysis of comics and their visual rhetoric, this study sheds light on the nuanced ways in which visual elements contribute to meaning-making in the medium. The findings of this research have implications for educators, artists, and scholars interested in understanding the role of visual rhetoric in communication and storytelling. Keywords: Comics, visual rhetoric, visual elements, interpretation, communication.