Author bibliographic coupling: Another approach to citation-based author knowledge network analysis

2008 ◽  
Vol 45 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Dangzhi Zhao ◽  
Andreas Strotmann
Keyword(s):  
Network Analysis ◽  
Knowledge Network ◽  
Bibliographic Coupling ◽  
Author Knowledge

scholarly journals Visualization of clinical teaching citations using social network analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hakimeh Hazrati ◽  
Shoaleh Bigdeli ◽  
Seyed Kamran Soltani Arabshahi ◽  
Vahideh Zarea Gavgani ◽  
Nafiseh Vahed
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Research Collaboration ◽  
Clinical Teaching ◽  
Clustering Coefficient ◽  
Knowledge Network ◽  
Research Network ◽  
Centrality Measures ◽  
The Usa

Abstract Background Analyzing the previous research literature in the field of clinical teaching has potential to show the trend and future direction of this field. This study aimed to visualize the co-authorship networks and scientific map of research outputs of clinical teaching and medical education by Social Network Analysis (SNA). Methods We Identified 1229 publications on clinical teaching through a systematic search strategy in the Scopus (Elsevier), Web of Science (Clarivate Analytics) and Medline (NCBI/NLM) through PubMed from the year 1980 to 2018.The Ravar PreMap, Netdraw, UCINet and VOSviewer software were used for data visualization and analysis. Results Based on the findings of study the network of clinical teaching was weak in term of cohesion and the density in the co-authorship networks of authors (clustering coefficient (CC): 0.749, density: 0.0238) and collaboration of countries (CC: 0.655, density: 0.176). In regard to centrality measures; the most influential authors in the co-authorship network was Rosenbaum ME, from the USA (0.048). More, the USA, the UK, Canada, Australia and the Netherlands have central role in collaboration countries network and has the vertex co-authorship with other that participated in publishing articles in clinical teaching. Analysis of background and affiliation of authors showed that co-authorship between clinical researchers in medicine filed is weak. Nineteen subject clusters were identified in the clinical teaching research network, seven of which were related to the expected competencies of clinical teaching and three related to clinical teaching skills. Conclusions In order to improve the cohesion of the authorship network of clinical teaching, it is essential to improve research collaboration and co-authorship between new researchers and those who have better closeness or geodisk path with others, especially those with the clinical background. To reach to a dense and powerful topology in the knowledge network of this field encouraging policies to be made for international and national collaboration between clinicians and clinical teaching specialists. In addition, humanitarian and clinical reasoning need to be considered in clinical teaching as of new direction in the field from thematic aspects.

Knowledge Network Analysis on Hallyu Research

2017 ◽  
Vol 61 (6) ◽  
pp. 318-353
Author(s):  
Seok-Kyeong Hong ◽  
Daemin Park ◽  
Sojeong Park
Keyword(s):  
Network Analysis ◽  
Knowledge Network

scholarly journals A tudásérték meghatározása minőségügyi szempontból, hálózatelemzési módszerekkel (The knowledge value in terms of quality with network analysis methods)

2012 ◽  
pp. 55-62
Author(s):  
Tamás Csiszér ◽  
József Cziráki
Keyword(s):  
Network Analysis ◽  
Quality Management ◽  
Knowledge Network ◽  
Negative Effects ◽  
Knowledge Loss ◽  
Network Nodes ◽  
Core Elements ◽  
Value Production ◽  
Knowledge Value ◽  
Management Issues

A minőségügy egyik kulcsfeladata, hogy azonosítsa az értékteremtés szempontjából kritikus tényezőket, meghatározza ezek értékét, valamint intézkedjen negatív hatásuk megelőzése és csökkentése érdekében. Az értékteremtés sok esetben folyamatokon keresztül történik, amelyek tevékenységekből, elvégzendő feladatokból állnak. Ezekhez megfelelő munkatársak kellenek, akiknek az egyik legfontosabb jellemzője az általuk birtokolt tudás. Mindezek alapján a feladat-tudás-erőforrás kapcsolatrendszer ismerete és kezelése minőségügyi feladat is. A komplex rendszerek elemzésével foglalkozó hálózatkutatás eszközt biztosíthat ehhez, ezért indokolt a minőségügyi területen történő alkalmazhatóságának vizsgálata. Az alkalmazási lehetőségek rendszerezése érdekében a szerzők kategorizálták a minőségügyi hálózatokat az élek (kapcsolatok) és a csúcsok (hálózati pontok) típusai alapján. Ezt követően definiálták a multimodális (több különböző csúcstípusból álló) tudáshálózatot, amely a feladatokból, az erőforrásokból, a tudáselemekből és a közöttük lévő kapcsolatokból épül fel. A hálózat segítségével kategóriákba sorolták a tudáselemeket, valamint a fokszámok alapján meghatározták értéküket. A multimodális hálózatból képzett tudáselem-hálózatban megadták az összefüggő csoportok jelentését, majd megfogalmaztak egy összefüggést a tudáselem-elvesztés kockázatának meghatározására. _______ The aims of quality management are to identify those factors that have significant influence on value production, qualify or quantify them, and make preventive and corrective actions in order to reduce their negative effects. The core elements of value production are processes and tasks, along with workforce having the necessary knowledge to work. For that reason the task-resource-knowledge structure is pertinent to quality management. Network science provides methods to analyze complex systems; therefore it seems reasonable to study the use of tools of network analysis in association with quality management issues. First of all the authors categorized quality networks according to the types of nodes (vertices) and links (edges or arcs). Focusing on knowledge management, they defined the multimodal knowledge network, consisting of tasks, resources, knowledge items and their interconnections. Based on their degree, network nodes can be categorized and their value can be quantified. Derived from the multimodal network knowledge-item network is to be created, where the meaning of cohesive subgroups is defined. Eventually they proposed a formula for determining the risk of knowledge loss.

scholarly journals Innovation and capabilities building in biopharmaceuticals in Brazil: a knowledge network analysis

2019 ◽  
Author(s):  
Nathalia Alves ◽  
Marina Szapiro ◽  
Marco Vargas ◽  
Jorge Britto
Keyword(s):  
Network Analysis ◽  
Knowledge Network

scholarly journals Text Mining and Citation Network of Ecosystem Services Publications

2019 ◽  
Author(s):  
Muhammad Malik Ar-Rahiem
Keyword(s):  
Ecosystem Services ◽  
Network Analysis ◽  
Text Mining ◽  
Biodiversity Conservation ◽  
Bibliometric Analysis ◽  
Natural Capital ◽  
Citation Network ◽  
Well Being ◽  
Bibliographic Coupling ◽  
Citation Network Analysis

Ecosystem Services is an important concept to achieve Sustainable Development Goals 2030. For the past 20 years, this concept has grown exponentially and the metadata of these publications can be considered as big data. A bibliometric analysis was conducted to Ecosystem Services publications from Web of Science database, which are text-mining analysis, bibliographic coupling, and citation network analysis. Text-mining analysis results were a cluster map of keywords representing the content of abstract and title from 4203 publications in the dataset. Bibliographic coupling analysis results were a cluster of documents which analyzed using natural language processing to extract the main idea of the documents. Using these two analysis insight about ecosystem services are obtained. Ecosystem services in general can be divided into 6 big clusters: economic assessment of ecosystem services as natural capital, ecosystem services assessment in term of accounting and management, biodiversity conservation in term of species richness, biodiversity conservation in term of human well-being, climate change and ecosystem services, and ecosystem services in urban area. Finally, citation network analysis was performed. 5700 publications consist of publications from the dataset and cited references from the publications were analyzed and 50 most influential articles from 1977 to 2018 with highest citation score was plotted in chronological order, providing insight on how the topic has been developing over time and important publications to be read. Bibliometric analysis proved to be very useful, especially as the preliminary step before conducting literature review. This technique can be very beneficial for early career scientists who wanted to recognize a field of science or wanted to know the research gaps that could be worked on.

scholarly journals ANALYSIS OF SCIENTIFIC PRODUCTION IN THE FIELD OF SUSTAINABLE TOURISM:

2021 ◽  
Vol 9 (3) ◽  
pp. 425-447
Author(s):  
Benedita Marta Gomes Costa ◽  
Patricia Veronica Pinheiro Sales Lima
Keyword(s):  
Sustainable Development ◽  
Network Analysis ◽  
Web Of Science ◽  
Sustainable Tourism ◽  
Scientific Production ◽  
Knowledge Network ◽  
Collaboration Network ◽  
Bibliometric Indicators ◽  
Tourism Management ◽  
The Web

The aim of this research was to investigate the dynamics of scientific production in the field of sustainable tourism, emphasizing the collaboration network, knowledge generated and the key authors and institutions that contribute to the advancement of knowledge.Information was collected from articles, books and proceeding papers using the Web of Science (WoS) platform from 1990 to 2018. A total of 7,051 documents were analyzed. Data were analyzed using network analysis and bibliometric indicators. Based on the information collected, scientific production linked on the economic pillar, particularly regarding terms such as sustainable tourism, tourism, management, sustainable development, sustainability, ecoturism and conservation. Thus, we found that the dynamics of collaboration in scientific production in sustainable tourism has a dense geographic network and proximity of themes as elements for structuring the knowledge network.

Social Network Analysis in Knowledge Management Research

2011 ◽  
Vol 474-476 ◽  
pp. 1007-1011
Author(s):  
Bing Wu ◽  
Jun Ge ◽  
Wen Xia Xu
Keyword(s):  
Knowledge Management ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
The United States ◽  
Library Science ◽  
Knowledge Network ◽  
Future Research ◽  
Management Research ◽  
Main Research

This study is a productivity review on the literature gleaned from SSCI, SCIE databases concerning social network analysis in knowledge management research. The result indicates that the number of related literature is still growing especially in recent two years. The main research development country is the United States, then England and German, and from the analysis of the subject area, Information Science & Library Science is the most popular subject. Concerning source title, Knowledge Management Research & Practice is in the priority. Moreover the research focuses on this topic are mainly in close relationship with knowledge network. Typical references were analyzed in detail, including limitations and future research.

Trailing organizational knowledge paths through social network lens: integrating the multiple industry cases

2014 ◽  
Vol 18 (1) ◽  
pp. 38-51 ◽  
Author(s):  
Young-Gul Kim ◽  
Yong Sauk Hau ◽  
Seulki Song ◽  
Ghi-Hoon Ghim
Keyword(s):  
Social Network ◽  
Network Analysis ◽  
Cultural Difference ◽  
Organizational Knowledge ◽  
Knowledge Flows ◽  
Knowledge Network ◽  
Knowledge Flow ◽  
Content Type ◽  
Management Level ◽  
Business Units

Purpose – This study aims at analyzing the features of knowledge flow and the role-specific nodes in knowledge networks among individuals and business units of six organizations in different industries, and suggesting prescriptions to prevent the organizational knowledge sclerosis. Design/methodology/approach – This research conducts multiple case studies on the organizational knowledge paths of six companies in the multiple industries through social network analysis (SNA) tool developed by the authors of this paper. Findings – This study provides four major findings which shed a new light on how to comprehend the features of knowledge flow and the role-specific nodes in knowledge networks in organizations: the within-business unit knowledge flows are more dominant over the inter-business units knowledge flow; the downward knowledge flows are dominant over the horizontal and upward knowledge flows in the management levels; distributions of knowledge owners and providers are like L-shape and the gap between knowledge owing and providing expands as the management levels go up; and the top 20 percent people in an organization dominate over a large portion of the knowledge brokerage activities. Research limitations/implications – Cultural difference issue might arise because data collection was limited to Korean organizations. Therefore, the findings from this study needs to be cautiously interpreted considering the cultural difference/deeper understanding of the organizational knowledge paths through social network lens can make it possible for more context-specific KM strategies (e.g. suitable for a specific functional unit, management level, or industry type) to be identified and implemented. Practical implications – Managers can have a solid grasp about knowledge flows and knowledge node roles in their organization through social network analysis in order to facilitate the knowledge transfer and eliminate the knowledge link lapse in organizations. Originality/value – This study could be a stepping stone for further empirical research since it expanded the level of organizational knowledge network analysis from individual and team to inter-unit and inter-management level through the block modeling analysis of knowledge network.

scholarly journals Developmental trajectories in blockchain technology using patent-based knowledge network analysis

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Sohee Kim ◽  
Sejun Yoon ◽  
Nagarajan Raghavan ◽  
Nguyen-Truong Le ◽  
Hyunseok Park
Keyword(s):  
Network Analysis ◽  
Developmental Trajectories ◽  
Knowledge Network ◽  
Blockchain Technology

scholarly journals Measuring popularity of ecological topics in a temporal dynamical knowledge network

2018 ◽  
Author(s):  
Tian-Yuan Huang ◽  
Bin Zhao
Keyword(s):  
Big Data ◽  
Network Analysis ◽  
Data Processing ◽  
Bibliometric Analysis ◽  
Exponential Growth ◽  
Domain Knowledge ◽  
Knowledge Network ◽  
Keyword Analysis ◽  
The Rich ◽  
Massive Information

AbstractAs interdisciplinary branches of ecology are developing rapidly in the 21st century, contents of ecological researches have become more abundant than ever before. Along with the exponential growth of number of published literature, it is more and more difficult for ecologists to get a clear picture of their discipline. Nevertheless, the era of big data has brought us massive information of well documented historical literature and various techniques of data processing, which greatly facilitates the implementation of bibliometric analysis on ecology. Frequency has long been used as the primary metric in keyword analysis to detect ecological hotspots, however, this method could be somewhat biased. In our study, we have suggested a method called PAFit to measure keyword popularity, which considered ecology-related topics in a large temporal dynamical knowledge network, and found out the popularity of ecological topics follows the “rich get richer” and “fit get richer” mechanism. Feasibility of network analysis and its superiority over simply using frequency had been explored and justified, and PAFit was testified by its outstanding performance of prediction on the growth of frequency and degree. In addition, our research also encourages ecologists to consider their domain knowledge in a large dynamical network, and be ready to participate in interdisciplinary collaborations when necessary.

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