Abstract
Social network analysis is commonly used to investigate relationships between a group of objects by modelling them using graphs. Some of its applications include network modelling and sampling, link prediction and social media analytics. In this study, we analyse research collaboration in between 44 researchers in the Department of Mathematics and Statistics, Universiti Putra Malaysia within two periods, from 2015–2017 and 2018–2020, by using social network analysis. We identify the importance of each researcher within the community by using different centrality measures such as degree, closeness, betweenness and eigenvector centralities, based on the collected data. The ten highest values in each measure for both durations are given. The graphs for each centrality measure are plotted, and a comparison on the relevant values across the two periods is made. We also discuss the relationship between each centrality measure, as well as the possible factors that affect the respective values.
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