Abstract
This project conducted link analysis and graph cluster analysis to analyze the co-authorship network of 166 researchers, mainly from three top universities in Shanghai, China. The publication data of researchers in the area of social science between 2014 and 2016 were collected from Scopus, and the g index was calculated as their performance indicator. For this project, the centrality measures, the efficiency of the egocentric network were calculated as well as authorities and hubs were identified in the link analysis. In addition, clustering algorithms based on betweenness centrality were used to conduct the graph cluster analysis. Finally, in order to identify productive researchers, this project employed the Spearman correlation test to analyze the correlation between a researcher's performance and social network measures. Results from this test indicate that except for closeness centrality and degree centrality, the correlation between g-index and betweenness centrality, eigenvector centrality and efficiency is significant.
| Original language | English |
|---|---|
| Title of host publication | IoTBDS 2020 - Proceedings of the 5th International Conference on Internet of Things, Big Data and Security |
| Editors | Gary Willis, Peter Kacsuk, Victor Chang |
| Publisher | SciTePress |
| Pages | 359-366 |
| Number of pages | 8 |
| ISBN (Electronic) | 9789897584268 |
| DOIs | |
| Publication status | Published - 2020 |
Keywords
- Co-authorship network
- academic performance
- social network analysis
- Spearman correlation test