Bibliometric Characteristics of Cryptocurrency through Citation Network Analysis

Objective To understand belief in a specific scientific claim by studying the pattern of citations among papers stating it.Design A complete citation network was constructed from all PubMed indexed English...

Abstract

Sustainability Science has been widely accepted as an important research field all over the world. However, Sustainability Science is widespread to many research field such as Environment Science and Engineering, Architecture, Material Sciences, Civil Engineering, etc., therefore, it is important to discover hidden connections between sustainability science and other technologies. At the same time, with increasingly strong determination towards sustainable society, the tools for supporting discovering the linkage between technologies are necessary. This paper investigates the relationships between Sustainability Science and Complex Networks based on the citation network analysis. First, citation networks are generated based on academic paper datasets, and conducted the clustering for understanding the research fields to the Sustainability Science and other technologies. Next, we measure the textual similarities between clusters in Sustainability Science and Complex Networks. After that, we find the middle layer research area for connecting Complex Networks and Sustainability Science using analysis of the shared terms. These results could offer overviews of how Complex Networks contributes to the sustainability science, and assist in forming policies to promote key technologies towards the future of sustainable society.

Finding influential agent groups in complex multiagent software systems based on citation network analyses

Sports associations and companies share a common goal of identifying and selecting top talent, prompting a growing interest in refining their selection processes. Recent decades have witnessed increased attention to predictive valid constructs (e.g., motivation) and predictive assessment methods (e.g., questionnaires). Leveraging an extensive history and empirical evidence from personnel selection research in business, a cross-contextual knowledge transfer could offer potential benefits for both sports and business. To examine the utilization of information between sports and the business context, this study explores the connection between these contexts. Analyzing 20,492 articles from SCOPUS and Web of Science databases, we followed PRISMA guidelines and conducted a citation network analysis (n = 940 articles). Results reveal limited interconnectivity (3,728 links), with only six articles cited between contexts: three sports articles cited business articles and vice versa. Sports articles referred to business research for talent definition, skill assessment, and methodological concerns. Contrarily, the business domain primarily used sports research as examples and for individual aspects of talent development models. This study highlights the potential for points of connection, including requirement analysis, skill assessment, and research on predictive validity. Future research should systematically explore shared and underrepresented topics in both contexts, such as the use of, for example, situational judgement tests in sports to enhance talent selection in sports, drawing insights from the business domain.Systematic review registrationhttps://aspredicted.org/366x-7cfd.pdf.

Background: Proper vision is absolutely critical to children’s academic performance, as vision problems can drastically affect learning ability. Currently, the existing literature in this field is somewhat inconsistent and carries several controversies about the influence of vision on academic performance. In this study, citation networks were utilized in order to analyze the relationship between publications and authors, the most-cited publication, and the different research areas. Additionally, the most commonly utilized publication sources along with the most common research areas were also pinpointed. Methods: The aforementioned search was executed in the Web of Science database, with a time range beginning in 1941 and ending in 2022. In order to scrutinize the publications, VOSviewer, CiteSpace software, and the Citation Network Explorer were utilized for analysis about the most-cited publication and the different research areas. Results: Overall, 1342 papers were found in all fields along with 2187 citation networks. Moreover, 2020 was the year with the most publications, including 127 publications and 4 citation networks. Bull et al., published in 2008, was the most-cited work, reaching a citation index of 975. The clustering function managed to identity four groups with the most engaging research topics from researchers: motor visual skills, visual memory, the influence of vision on the different learning styles, and abnormal development of the visual system. Conclusions: In the end, the topic with the greatest interest was the influence of visual motor skills on academic performance. Ideally, this paper will assist fellow researchers in quickly noting which topics are of greatest interest and how research in this area has evolved.

In this study, we propose a framework for detecting topic evolutions in weighted citation networks. Citation networks are important in studying knowledge flows; however, citation network analysis has primarily focused on binary networks in which the individual citation influences of each cited paper in a citing paper are considered identical, even though not all cited papers have a significant influence on the cited publication. Accordingly, it is necessary to build and analyze a citation network comprising scholarly publications that notably impact one another, thus identifying topic evolution in a more precise manner. To measure the strength of citation influence and identify paper topics, we employ a citation influence topic model primarily based on topical inheritance between cited and citing papers. Using scholarly publications in the field of the protein p53 as a case study, we build a citation network, filter it using citation influence values, and examine the diffusion of topics not only in the field but also in the subfields of p53.

We propose a method of citation analysis for evaluating the topic-dependent importance of individual scientific papers. This method assumes spreading activation in citation networks with a multi-hysteretic input/output relationship for each node (paper). The multi-hysteretic property renders the steady state of spreading activation continuously dependent on the initial state. Given a topic represented by the initial state, the importance of individual papers can be defined by the activities they have in the steady state. We have devised this method inspired by memory retrieval in the brain, where the multi-hysteretic property of single cells or neuronal networks is considered to play an essential role for cue-dependent retrieval of memory. Quantitative evaluation using a restoration problem has revealed that the performance of the proposed method is considerably higher than that of the benchmark method. We demonstrate the practical usefulness of the proposed method by applying it to a citation network of neuroscience papers.KeywordsBibliometricsScientometricsCitation networkPageRankMemory retrievalContinuous attractorGraded persistent activity

Patent Citation Analysis has been gaining considerable traction over the past few decades. In this paper, we collect extensive information on patents and citations and provide a perspective of citation network analysis of patents from a statistical viewpoint. We identify and analyze the most cited patents, the most innovative and the highly cited companies along with the structural properties of the network by providing in-depth descriptive analysis. Furthermore, we employ Exponential Random Graph Models (ERGMs) to analyze the citation networks. ERGMs enables understanding the social perspectives of a patent citation network which has not been studied earlier. We demonstrate that social properties such as homophily (the inclination to cite patents from the same country or in the same language) and transitivity (the inclination to cite references’ references) together with the technicalities of the patents (e.g., language, categories), has a significant effect on citations. We also provide an in-depth analysis of citations for sectors in patents and how it is affected by the size of the same. Overall, our paper delves into European patents with the aim of providing new insights and serves as an account for fitting ERGMs on large networks and analyzing them. ERGMs help us model network mechanisms directly, instead of acting as a proxy for unspecified dependence and relationships among the observations.

ABSTRACTPublic policy often builds on a large body of policy advice—produced by bureaucrats, advisory bodies, scientists, and consultants—that has accumulated over time about a specific policy problem and appropriate policy responses. Yet, how this body of policy advice develops and which pieces of advice influence subsequent policy recommendations is poorly understood. We seek to fill this gap by proposing a novel conceptualization and methodological approach for unpacking the influence of policy advice on other policy advice. First, we distinguish four conceptual dimensions along which the influence of policy advice varies: lasting influence, broad influence, influential followers, and influence as broker. Second, we propose citation network analysis as a methodological tool for measuring these multiple dimensions of influence. We present a set of citation network measures of influence and illustrate their usefulness by analyzing influence patterns within a large network of Norwegian advisory reports.

BackgroundEstablishing a horizon scanning method is critical for identifying technologies that require new guidelines or regulations. We studied the application of bibliographic citation network analysis to horizon scanning.ObjectiveThe possibility of applying the proposed method to interdisciplinary fields was investigated with the emphasis on tissue engineering and its example, three-dimensional bio-printing.Methodology and ResultsIn all, 233,968 articles on tissue engineering, regenerative medicine, biofabrication, and additive manufacturing published between January 1, 1900 and November 3, 2021 were obtained from the Web of Science Core Collection. The citation network of the articles was analyzed for confirmation that the evolution of 3D bio-printing is reflected by tracking the key articles in the field. However, the results revealed that the major articles on the clinical application of 3D bio-printed products are located in clusters other than that of 3D bio-printers. We investigated the research trends in this field by analyzing the articles published between 2019 and 2021 and detected various basic technologies constituting tissue engineering, including microfluidics and scaffolds such as electrospinning and conductive polymers. The results suggested that the research trend of technologies required for product development and future clinical applications of the product are sometimes detected independently by bibliographic citation network analysis, particularly for interdisciplinary fields.ConclusionThis method can be applied to the horizon scanning of an interdisciplinary field. However, identifying basic technologies of the targeted field and following the progress of research and the integration process of each component of technology are critical.

Greater attention than ever is being placed on how universities enable their graduates to achieve their career goals as a key return on significant private and public investment. Scholars in two distinct fields of research have explored the characteristics and conditions that promote or constrain graduates’ career success: graduate employability and career development. In this article, we present visualisations of direct citation networks among 4068 journal articles focused on graduate employability and career development and consider the disciplinary landscapes that they reveal. Our findings show that despite a clear alignment of research concerns and educational goals, there has been limited theoretical or practical exchange between the two fields. We argue that purposeful exchange between the two fields will enrich both and, when applied to practice, could inform an evidence-based, integrative pedagogy of careers and employability learning in higher education.

PurposeThe purpose of this paper is to present a comprehensive analysis of the current status and development of the digital journalism field from 1987 to 2021 using the Dimensions database.Design/methodology/approachUsing the Dimensions.ai database, 1734 articles were identified through search strategies which were published from 1987 to 2021. The downloaded results were analysed using specific parameters with the help of bibliometric and science mapping tools: Biblioshiny, VOSviewer and CiteSpace. The key contributions of the present comprehensive bibliometric study of the digital journalism field can be seen in terms of the following aspects: (1) Publication analysis from the perspectives of publication growth, key journals, contributing authors, institutions and countries done through Biblioshiny package. (2) Citation network analysis from the perspective of co-citation structure of papers, authors, countries and institutions done through VOSviewer. (3) Timeline analysis and keywords burst detection to identify hotspots and research trends in digital journalism with the help of CiteSpace.FindingsThe first paper with the keyword digital journalism was published in the year 1989. From 2011 onwards, there has been growth in digital journalism literature. The most popular journal in digital journalism studies is Digital Journalism, Journalism, Journalism Practice, Journalism Studies. Lewis, S.C. has contributed the most number of papers in digital journalism. Further, authors from the countries the USA, Spain, Brazil and UK have contributed immensely. The citation network of authors, institutions and countries contributing to digital journalism studies has also been explored in the study. Through burst analysis, hot topics in digital journalism were identified.Originality/valueThe paper provides a complete overview of the growth of digital journalism literature published from 1987 to 2021. The originality of this work lies in the triangulation of Biblioshiny, VOSviewer and CiteSpace software to present various aspects of bibliometric study. Findings of the study can help the researchers to identify areas as well as journals, authors, institutions working actively in the field of digital journalism.

Background: For many years it has been studied how eye movements influence reading andlearning ability. The objective of this study is to determine the relationships between thedifferent publications and authors. As well as to identify the different areas of research ocularmovement.; Methods: Web of Science was the database for the search of publicationsfor the period 1900 to May 2021, using the terms: “Eye movement" AND “Academicachiev*”. The analysis of the publication was performed using the CitNetExplorer,VOSviewer and CiteSpace software.; Results: 4391 publications and 11033 citation networkswere found. The year with the most publications is 2018, a total of 318 publicationsand 10 citation networks. The most cited publication was "Saccade target selection and objectrecognition: evidence for a common attentional mechanism." published by Deubel et al.in 1999, with a citation index of 214. Using the Clustering function, nine groups were foundthat cover the main research areas in this field: neurological, age, perceptual attention, visualdisturbances, sports, driving, sleep, vision therapy and academic performance.; Conclusion:Even being a multidisciplinary field of study, the topic with the most publications to date isthe visual search procedure at the neurological level.

Mapping socially responsible investing: A bibliometric and citation network analysis