scholarly journals Exploration Knowledge Sharing Networks Using Social Network Analysis Methods

2017 ◽  
Vol 10 (3) ◽  
pp. 179-191
Author(s):  
Győző Attila Szilágyi
2021 ◽  
Vol 13 (1) ◽  
pp. 31-44
Author(s):  
Arbana Kadriu ◽  
Kosovare Sahatqija ◽  
Lejla Abazi-Bexheti

The purpose of the research presented in this paper is the investigation of the gender gap in published computing books. The book titles from the DBLP computer science bibliography were the basis for this investigation. The conducted research involves co-authorship network exploration using social network analysis methods, as well as content learning by keyword extraction and ranking from book titles. The findings show that female authors tend to publish fewer books in computing than their male colleagues, and there is a huge gap of women regarding the collaboration. There are just two women names within the 50 author names with the highest social network top metrics, indicating collaboration. Regarding the extracted keywords, though there are differences, results do not show some huge divergences when it comes to the used language for computing titles.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jiayuan Liu ◽  
Jianzhou Yan

PurposeThis study examines the relationships between structural holes, guanxi and knowledge sharing among groups of stakeholders within a Chinese destination network.Design/methodology/approachThis study conducted surveys, social network analysis and semi-structured interviews to gather data from the stakeholders of a popular Chinese tourist destination to test its hypotheses.FindingsKnowledge sharing within the destination network was impeded by structural holes but facilitated by guanxi. Furthermore, the impeding effect of structural holes on knowledge sharing is alleviated by guanxi.Originality/valueThis study illustrates the ways that stakeholders exploit structural holes and guanxi to promote knowledge sharing, and thus offers novel insights into how destination network structures affect the efficacy of stakeholders when it comes to sharing knowledge and promoting their destination.


Graphs are mathematical formalisms that represent social networks very well. Analysis methods using graph theory have started to develop substantially along with the advancement of mathematics and computer sciences in recent years, with contributions from several disciplines including social network analysis. Learning how to use graphs to represent social networks is important not only for employing theoretical insights of this advanced field in social research, but also for the practical purposes of utilizing its mature and abundant tools. This chapter explores structural analysis with graphs.


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