Co-authorship Network Research Articles

Global Research Trends in Green Finance: A Systematic Literature Review of Regional Contributions (2001-2024)

This study presents the systematic analysis of existing literature on global research trends in green finance, focusing on regional contributions from 2001 to 2024. Using data from the Web of Science and Scopus databases, this analysis identifies patterns in publication volume, regional leadership, and thematic evolution in green finance research. The findings reveal a substantial increase in scholarly output, particularly in response to climate agreements such as the Paris Agreement and the Sustainable Development Goals. Notably, countries like China, the United Kingdom, and emerging contributors, including Pakistan, are leading in research output. The study underscores a shift from foundational studies on sustainable finance to specific applications in green bonds, ESG criteria, and climate finance. Additionally, it highlights strong international co-authorship networks, especially among researchers in Europe, North America, and Asia, which facilitate cross-border collaboration and knowledge dissemination.

Mindfulness at Work: A Bibliometric Analysis of Key Authors, Themes, and Trends

Background: Mindfulness in the workplace has gained significant attention for its potential to enhance well-being, reduce stress, and improve productivity. As interest in workplace mindfulness has grown, a need arises to systematically analyze the influential studies, key authors, themes, and trends shaping this field. Objectives: This study aims to map the intellectual landscape of "mindfulness at work" through a bibliometric analysis, identifying key contributions, collaboration networks, thematic clusters, and publication trends over time. Methods: Utilizing data from Lens.org, we conducted a bibliometric analysis using VOSviewer and WordSift tools to examine co-authorship, keyword co-occurrence, and citation networks, as well as keyword frequency and thematic trends. Findings: The analysis identifies central figures such as Jochen Reb and Christopher J. Lyddy and highlights prominent themes including well-being, stress management, and engagement, with a peak in publication interest around 2019. The study also reveals isolated research clusters, suggesting limited interdisciplinary integration within the field. Conclusion: While mindfulness research at work is growing, opportunities exist for deeper cross-disciplinary collaboration and broader integration of themes. Novelty: This study provides a comprehensive overview of the field’s development, offering insights into emerging trends, key influencers, and areas for future exploration, making it a valuable resource for researchers, practitioners, and policy-makers interested in advancing workplace mindfulness.

A bibliometric analysis of research on physical activity and fitness among preschool children in Asia (2020-2024)

Background: Physical activity (PA) and physical fitness (PF) are essential for preschool children's development. However, rising childhood obesity and sedentary lifestyles are concerning. A comprehensive analysis of the research landscape is necessary to inform better interventions and policies. Research Objectives: This study aims to conduct a bibliometric analysis of research on physical activity and physical fitness among preschool children in Asia from 2020 to 2024. Methods: The analysis was based on publications from Scopus and Web of Science, focussing on keyword co-occurrence, citation analysis, and co-authorship networks. We used tools like ScientoPy and VOSviewer to assess key metrics such as the number of publications, citations, and influential contributors. Findings/Results: The study reveals significant trends, with a peak in research productivity in 2022, reflecting heightened global attention on early physical health. China, Japan, and Iran led contributions, with China dominating in publication volume. Notable researchers included Saeed Ghorbani and emerging contributors Ahmad Chaeroni and Amir Dana. Leading journals, such as the International Journal of Environmental Research and Public Health and Frontiers in Public Health, were prominent sources. Conclusion: Despite a decline in productivity post-2022, PA and PF research for preschoolers remains important. These shifting trends provide opportunities for researchers to explore new areas or innovate approaches. Significant contributions from Asian researchers also indicate a geographic shift in the global research focus. This study also highlights the importance of physical activity in preschoolers and how trends in scientific publications can help policymakers formulate more effective interventions to improve children's fitness.

A model to address the cold-start in peer recommendation by using k-means clustering and sentence embedding

In academia, research collaboration plays a vital role in enhancing the research quality and enriching the academic profile of the authors. Recommending appropriate collaborators from a vast scholarly database, particularly for newcomers, poses a challenging cold-start problem. This study addresses a cold-start problem in peer recommendation, considering a dynamic coauthorship graph as a network structure of academic collaborators. As the coauthorship graph is quite large and complex, an efficient indexing method is essential for speeding up the initial search of similar coauthors. The study introduces an efficient Global Inverted List (GIL) for indexing research areas and active authors in the coauthorship network. An attribute-based search and filtering mechanism is proposed to identify relevant collaborators, followed by the application of k-means clustering and doc2vec metrics to rank and select top recommendations. A cold user is associated with attributes that identify coauthors with similar research interests. For each attribute of the cold user, the model searches the associated authors from the GIL. Further, two filtering approaches are applied to refine the retrieved author list. The first ensures that the authors have a significant presence in the specified research areas, whereas the second one helps avoid recommending authors with only superficial connections to the cold user. The model creates a feature matrix of filtered authors using the publication features of authors. The k-means clustering applied to the feature matrix generates k clusters, among which the model chooses only those with seed nodes i.e. the clusters which are having seed nodes are selected for further process. Selected clusters are ranked using doc2vec metrics, with the top-ranked cluster providing the final recommendation. The model recommends the top L members of the selected cluster, where L is the length of the recommendations provided to the new user. Our extensive experiments show the efficacy of the proposed model.

The Role of Sharia Economics in Realizing Sustainable Development in Indonesia

This study provides a comprehensive bibliometric analysis of the role of Sharia economics in promoting sustainable development in Indonesia. By examining 810 publications between 2006 and 2024, the study explores key research trends, themes, and collaborations in the literature. The analysis reveals a significant increase in research activity after 2020, with a focus on aligning Islamic finance principles with sustainable development goals (SDGs), particularly in areas such as poverty alleviation, economic growth, and green finance. Keyword co-occurrence visualizations highlight the importance of concepts such as "green sukuk," "economic growth," and "poverty alleviation," while co-authorship networks show concentrated collaboration within small research groups. The findings suggest that Sharia economics has substantial potential to contribute to SDGs, particularly through instruments like green sukuk and waqf, but greater interdisciplinary collaboration and exploration of emerging topics like technology and post-pandemic recovery are needed. These insights have significant implications for both academic research and policy-making in Indonesia and beyond.

Academic collaboration recommendation based on graph neural network and multi-attribute embedding

Academic collaboration recommendation (ACR) can help researchers find potential partners for research and thus promote academic innovation. Recent works mostly use graph learning-based methods to explore various ways of combining node information with topology, which consists of multiple steps, including network construction, node feature extraction, network representation learning and link prediction. One limitation is that they only conduct research on co-authorship networks and ignore citation relationship between publications. Besides, they tend to use attributes from a single dimension of researchers and do not take attributes of researchers from multiple dimensions into consideration at the same time. To address the above issues, we present the multi-dimensional attributes enhanced heterogeneous (MAH) network representation learning method, which constructs heterogeneous networks with both co-authorship and citation information and makes use of multi-dimensional attributes. Three research questions are addressed in this work: (RQ1) Is our proposed method effective on academic collaboration recommendation compared with existing state-of-the-art methods? (RQ2) Does incorporating citation information into co-authorship network help improve the performance of academic collaboration recommendation? (RQ3) How does fusing multi-dimensional attributes affect the performance of academic collaboration recommendation? A publicly available real-world data set is used in our experiments. The superior performance of MAH compared with baseline methods demonstrates that the proposed multi-dimensional feature-based researcher profile can enrich node information in academic network and effective researcher representations can be learned by applying graph representation learning methods on the network.

Financial Development and Climate Change: A Detailed Bibliometric Investigation

This paper presents a detailed bibliometric analysis of the interaction between financial development and climate change, with the main aim of elucidating the current state of research in this area, identifying existing gaps, and guiding future researchers interested in this rapidly expanding field. The study used VOSviewer software version 1.6.18 to analyze the bibliometric data, facilitating the mapping of co-author networks, institutional collaborations, and the identification of main research directions. Through this tool, 730 papers from the Web of Science database covering the period 2010–2024 were extracted and analyzed. The study highlights the authors and institutions that have made significant contributions to the investigation of the relationship between financial development and climate change. The identification of key contributors and international collaborative networks provides a solid foundation for future research and policy initiatives. In addition, the study identifies the most prolific journals and assesses the quality and impact of the research. This allows for a deeper understanding of current research directions and potential future developments. The study not only clarifies the current state of research but also opens up new opportunities for investigating innovative and sustainable solutions aimed at improving the quality of life and protecting the environment.

A Review on Machine Learning-Aided Hydrothermal Liquefaction Based on Bibliometric Analysis

Hydrothermal liquefaction (HTL) is an effective biomass thermochemical conversion technology that can convert organic waste into energy products. However, the HTL process is influenced by various complex factors such as operating conditions, feedstock properties, and reaction pathways. Machine learning (ML) methods can utilize existing HTL data to develop accurate models for predicting product yields and properties, which can be used to optimize HTL operation conditions. This paper presents a bibliometric review on ML applications in HTL from 2020 to 2024. CiteSpace, VOSviewer, and Bibexcel were used to analyze seven key bibliometric attributes: annual publication output, author co-authorship networks, country co-authorship networks, co-citation of references, co-citation of journals, collaborating institutions, and keyword co-occurrence networks, as well as time zone maps and timelines, to identify the development of ML in HTL research. Through the detailed analysis of co-occurring keywords, this study aims to identify frontiers, research gaps, and development trends in the field of ML-aided HTL.

Bibliometric Insights into the "Kathmandu School of Law Review"

This bibliometric study analyzes the academic reach, thematic focus, and collaboration trends of the Kathmandu School of Law Review (KSLR). Through keyword analysis, the research highlights recurring themes such as human rights, governance, policy-making, and development, reflecting a multidisciplinary approach to legal scholarship. Visualizations of co-authorship networks reveal limited collaboration among scholars, suggesting opportunities for the journal to foster joint publications and interdisciplinary partnerships. Citation network analysis indicates a niche area of research with a few central authors like Puja Mitra and Yubaraj Sangroula, while the low overall citation count points to the need for broader academic engagement and visibility. Bibliographic coupling maps show emerging research clusters, indicating the potential for KSLR to build authority in key areas such as South Asian legal systems and international human rights. The study concludes with recommendations for KSLR to increase global outreach, promote interdisciplinary studies, and enhance academic collaboration to strengthen its impact within and beyond the region.

Co-authorship Networks in Left-Behind Children Research in China: A Social Network Analysis

Co-authorship Networks in Left-Behind Children Research in China: A Social Network Analysis

Trends of machine learning for dental caries research in Southeast Asia: insights from a bibliometric analysis.

Dental caries is a common chronic oral disease, posing a serious public health issue. By analyzing large datasets, machine learning shows potential in addressing this problem. This study employs bibliometric analysis to explore emerging topics, collaborations, key authors, and research trends in Southeast Asia related to the application of machine learning in dental caries management. A comprehensive selection using the Scopus database to obtain relevant research, covering publications from inception to July 2024 was done. We employed the Bibliometric approaches, including co-authorship networks, yearly publishing trends, institutional and national partnerships, keyword co-occurrence analysis, and citation analysis, for the collected data. To explore the visualization and network analysis, we employed the tools such as VOSviewer and Bibliometrix in R package. The final bibliometric analysis included 246 papers. We found that Malaysia became the top contributor with 59 publications, followed by Indonesia (37) and Thailand (29). Malaysia had the highest Multiple Country Publications (MCP) ratio at 0.407. Top institutions including the Universiti Sains Malaysia led with 39 articles, followed by Chiang Mai University (36) and the National University of Singapore (30) became the leader. Co-authorship analysis using VOSviewer revealed six distinct clusters. A total of 1220 scholars contributed to these publications. The top 10 keywords, including 'human' and 'dental caries,' indicated research hotspots. We found growing evidence of machine learning applications to address dental caries in Southeast Asia. The bibliometric analysis highlights key authors, collaborative networks, and emerging topics, revealing research trends since 2014. This study underscores the importance of bibliometric analysis in tackling this public health issue.

Finding the new potential research on diabetic kidney disease and hemodialysis in healthcare insurance databases: A bibliometric analysis

To the best of our knowledge, bibliometric analysis has not been performed for studies related to diabetic kidney disease (DKD) and hemodialysis using healthcare big data. Herein, the aim of this bibliometric analysis was to identify emerging research trends in DKD and hemodialysis within healthcare insurance databases by exploring authors, co-author networks, and countries to discover new potential research areas. A bibliometric study was conducted, utilizing data obtained from the Scopus database. Keywords such as diabetic kidney disease, hemodialysis, insurance or big data, and prediction were employed. Inclusion criteria were original articles and review articles written in English published between 2010 and 2022. VOSviewer and the Bibliometrix package in R were used for comprehensive bibliometric analysis. VOSviewer facilitated keyword co-occurrence analysis to identify clusters and visualize relationships among keywords, emphasizing distinct research themes, keyword density, and network visualization. Meanwhile, Bibliometrix allowed exploration of key metrics such as prolific authors and institutions, publication trends, co-authorship networks, citations, document types, emerging trends through keyword analysis, and network visualizations, including co-authorship and keyword co-occurrence. Results from both tools were integrated for a thorough analysis. The present study yielded 2,199 articles, which was reduced to 1,828 after removing duplicates and applying inclusion criteria. This bibliometric analysis found that machine learning and artificial intelligence are emerging yet remain relatively under-researched in the context of hemodialysis and DKD. The prominence of topics such as diabetic nephropathy, non-insulin treatments, and lifestyle modifications highlighted ongoing research priorities in DKD and hemodialysis. Taiwan's dominance in publications suggested robust research activity in this field, while international collaboration underscored global interest and the potential for diverse research perspectives. The need for similar research development in Indonesia, leveraging big data and machine learning, indicates opportunities for advancing the understanding and management of DKD and hemodialysis within the region.

Insights and networks: methodological assessment and scientometric analysis of economic evaluations in dentistry

IntroductionAssessing the methodological quality of economic evaluations (EEs) is crucial for evidence-based decision-making. The study aimed to evaluate EEs in restorative dentistry and endodontics, while also analyzing the scientific landscape of researchers and publications through co-authorship and citation network analysis providing an insight into the distribution of scientific expertise.MethodologyA systematic search for relevant articles from 2012 to 2022 was conducted using PubMed, Scopus, and EBSCO. The ten-point Drummond checklist was used to appraise the methodological quality of included studies. Bibliometric data for network analysis were extracted from the Dimensions database and visualized using VOSviewer software.ResultsOf the 37 articles, 81.08% scored good, 16.21% average, and 2.7% poor on the methodological rating scale. Most of the included studies were in Q1 journals, with limited representation in Q2 and Q3 journals. Compliance was highest in Q2 journals (95%), followed by Q1 (88.36%), while it dropped to 40% for Q3 journals. Co-authorship analysis revealed a dense network of researchers, with Prof. Falk Schwendicke V. having a significant influence. Moreover, the Journal of Dentistry had the highest impact, followed by Journal of Endodontics and BMC Oral Health.ConclusionsDespite a diverse scientific landscape, participation from developing countries was limited emphasizing the need for inclusivity and diversity in the scientific network. While the quantity of good-quality studies was encouraging, the overall quality of evidence remains paramount for decision-making in healthcare policy and practice. Therefore, continuous efforts to improve methodological rigor and reporting practices are essential to contribute robust evidence.

Trends of machine learning for dental caries research in Southeast Asia: insights from a bibliometric analysis

Background Dental caries is a common chronic oral disease, posing a serious public health issue. By analyzing large datasets, machine learning shows potential in addressing this problem. This study employs bibliometric analysis to explore emerging topics, collaborations, key authors, and research trends in Southeast Asia related to the application of machine learning in dental caries management. Methods A comprehensive selection using the Scopus database to obtain relevant research, covering publications from inception to July 2024 was done. We employed the Bibliometric approaches, including co-authorship networks, yearly publishing trends, institutional and national partnerships, keyword co-occurrence analysis, and citation analysis, for the collected data. To explore the visualization and network analysis, we employed the tools such as VOSviewer and Bibliometrix in R package. Results The final bibliometric analysis included 246 papers. We found that Malaysia became the top contributor with 59 publications, followed by Indonesia (37) and Thailand (29). Malaysia had the highest Multiple Country Publications (MCP) ratio at 0.407. Top institutions including the Universiti Sains Malaysia led with 39 articles, followed by Chiang Mai University (36) and the National University of Singapore (30) became the leader. Co-authorship analysis using VOSviewer revealed six distinct clusters. A total of 1220 scholars contributed to these publications. The top 10 keywords, including ‘human’ and ‘dental caries,’ indicated research hotspots. Conclusion We found growing evidence of machine learning applications to address dental caries in Southeast Asia. The bibliometric analysis highlights key authors, collaborative networks, and emerging topics, revealing research trends since 2014. This study underscores the importance of bibliometric analysis in tackling this public health issue.

Kidney medicine meets computer vision: a bibliometric analysis.

Rapid advances in computer vision (CV) have the potential to facilitate the examination, diagnosis, and treatment of diseases of the kidney. The bibliometric study aims to explore the research landscape and evolving research focus of the application of CV in kidney medicine research. The Web of Science Core Collection was utilized to identify publications related to the research or applications of CV technology in the field of kidney medicine from January 1, 1900, to December 31, 2022. We analyzed emerging research trends, highly influential publications and journals, prolific researchers, countries/regions, research institutions, co-authorship networks, and co-occurrence networks. Bibliographic information was analyzed and visualized using Python, Matplotlib, Seaborn, HistCite, and Vosviewer. There was an increasing trend in the number of publications on CV-based kidney medicine research. These publications mainly focused on medical image processing, surgical procedures, medical image analysis/diagnosis, as well as the application and innovation of CV technology in medical imaging. The United States is currently the leading country in terms of the quantities of published articles and international collaborations, followed by China. Deep learning-based segmentation and machine learning-based texture analysis are the most commonly used techniques in this field. Regarding research hotspot trends, CV algorithms are shifting toward artificial intelligence, and research objects are expanding to encompass a wider range of kidney-related objects, with data dimensions used in research transitioning from 2D to 3D while simultaneously incorporating more diverse data modalities. The present study provides a scientometric overview of the current progress in the research and application of CV technology in kidney medicine research. Through the use of bibliometric analysis and network visualization, we elucidate emerging trends, key sources, leading institutions, and popular topics. Our findings and analysis are expected to provide valuable insights for future research on the use of CV in kidney medicine research.

Exploring the Frontiers of Mathematical Neuroscience: A Comprehensive Bibliometric Analysis.

Mathematical neuroscience is the branch of interdisciplinarity between mathematical modeling and neuroscience through computational techniques to study the structure, function, and dynamics of the brain. The objective of this paper is to undertake a comprehensive review of research trends in mathematical neuroscience and important developments in the period from 1973 to 2024. From this source of bibliographic data, Scopus alone returns 727 retrieved documents, consisting of journals, book chapters, and conference papers. The analysis showed an annual growth rate of 6.51% in this field and significant contributions from authors of 1957 sources. Specific tools were used in this review for in-depth analysis of publication patterns, co-authorship networks, keyword co-occurrences, and thematic evolution within this discipline. The most influential authors, dominant publication sources, and most active countries in the field were identified. The survey also underlines various other emerging trends, of which the highly increasing approach to the integration of machine learning and artificial intelligence (AI) with mathematical neuroscience is certainly the most interesting. Results highlight the dynamic and collaborative features of research in this area and provide insight into the intellectual landscape for research in this field, along with its future directions.

A BIBLIOMETRIC ANALYSIS OF RESEARCH ON DROPOUT IN OPEN AND DISTANCE LEARNING

The purpose of this study is to examine research on school dropout in open and distance education in the Web of Science (WoS) database using bibliometric analysis and to reveal trends in this area. In line with this goal, a total of 1,615 studies published between 1980 and 2022 were identified in the Web of Science (WoS) indexes. Descriptive and evaluative bibliometric methods were employed in the analysis of these publications, and the results were visualized using VOSviewer software. According to the research findings, studies on school dropout in open and distance education intensified in 2019. The analysis revealed that E.T. Pascarella is the most cited author, and F.D. Pereira has the highest co-authorship network. An examination based on institutions showed that the Universitat Autònoma de Barcelona has the highest number of publications, while the University of Michigan ranks first in terms of citation numbers. Furthermore, in terms of publication productivity and citation numbers, the United States is ranked first, followed by Spain. Looking at the keywords used in articles on the topic, early studies emphasized concepts such as “distance education”, “student retention”, “attrition”, “student success”, “social integration”, “academic integration”, “survival analysis”, “education policy”, “graduation”, and “financial aids”. In subsequent years, these were replaced by terms such as “learning analytics”, “educational data mining”, “systematic review”, “engagement”, “drop prediction”, “decision tree”, “student performance”, “gamification”, “massive open online course- MOOC”, and “artificial intelligence”. In this context, discussions were conducted within the framework of the literature, and various recommendations were provided based on the obtained findings.

Analysis of Effects on Scientific Impact Indicators Based on Coevolution of Coauthorship and Citation Networks

This study investigates the coevolution of coauthorship and citation networks and their influence on scientific metrics such as the h-index and journal impact factors. Using a preferential attachment mechanism, we developed a model that integrated these networks and validated it with data from the American Physical Society (APS). While the correlations between reference counts, paper lifetime, and team sizes with scientific impact metrics are well-known, our findings demonstrate how these relationships vary depending on specific model parameters. For instance, increasing reference counts or reducing paper lifetime significantly boosts both journal impact factors and h-indexes, while expanding team sizes without adding new authors can artificially inflate h-indexes. These results highlight potential vulnerabilities in commonly used metrics and emphasize the value of modeling and simulation for improving bibliometric evaluations.

Bibliometric Analysis of the Development of Entrepreneurial Ecosystem Research in Global Publications

This study conducts a comprehensive bibliometric analysis of the field of entrepreneurial ecosystems, mapping the evolving landscape of research from 1995 to 2024. Utilizing VOSviewer for visual network analysis, the research identifies central themes, examines co-authorship and institutional collaboration networks, and highlights the temporal progression of key topics. The findings reveal "entrepreneurial ecosystem" as a nexus connecting diverse research themes such as innovation, economic growth, and sustainability. Significant emphasis is placed on the integration of digital technologies and sustainability within entrepreneurial practices, reflecting shifting academic and practical priorities. The analysis of co-authorship networks shows robust collaboration among leading scholars and institutions, predominantly from Western regions, with increasing contributions from a global cohort. The study underscores the importance of incorporating diverse perspectives to fully understand the multifaceted nature of entrepreneurial ecosystems and suggests future research directions focusing on digital transformation and sustainability in entrepreneurship.

Global Trends in Employee Wellbeing Research: A Bibliometric Study

This bibliometric study examines global trends in employee wellbeing research from 1990 to 2024, focusing on key themes such as burnout, work engagement, job satisfaction, leadership, and workplace environment. Using co-authorship networks, keyword co-occurrence analysis, and citation metrics, the study identifies influential authors, emerging trends, and underexplored areas in the field. Results show a significant increase in publications since 2017, with a growing focus on the psychological and emotional dimensions of wellbeing. Key findings highlight the positive impact of transformational leadership and supportive work environments, as well as the detrimental effects of toxic behaviors like abusive supervision and workplace bullying. The study also identifies gaps in the literature, particularly regarding the intersection of employee wellbeing and diversity, equity, and inclusion (DEI). This analysis provides a foundation for future research and emphasizes the critical role of employee wellbeing in fostering organizational success and sustainability.