Open AccessExploratory Experiment on Co-Authorship Network using Social Network Analysis Metrics and Measures
Author(s) -
Pritheega Magalingam,
Ganthan Narayana Samy,
Nurazean Maarop,
Wan Nazirul Hafeez Wan Safie,
Muhammad Rijal,
Lim Yee Fang,
Abdullah Sakib,
Muhammad Yassin
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i4.35.23108
Subject(s) - centrality , social network analysis , network analysis , clique , data science , computer science , network science , measure (data warehouse) , organizational network analysis , social network (sociolinguistics) , data mining , structural holes , process (computing) , network theory , complex network , knowledge management , world wide web , psychology , sociology , mathematics , social psychology , statistics , social media , social science , social capital , organizational learning , physics , quantum mechanics , operating system
This paper contributes in understanding and gaining meaningful insight about the relationship among the scientist in the co-authorship network using social network analysis. We argue that the relationship analysis is not always a straightforward process. In the past one single measure, for example, the egocentric or centrality measure was used to describe the scientific collaboration patterns separately. In this paper, various analysis such as centrality analysis, ego network, community detection, largest clique and word frequency have been used to examine and interpret the collaboration among the authors. This research is not dominated by known researchers but involves an overall exploration of the network. Our research is mainly guided by the creation of research issues, assessing the type of dataset and the objectives for presenting the co-authorship relationships. It is important to identify the motive of the selected measures in order to achieve the predefined objective. Specific methodology and procedures are designed to solve each research issue respectively. This study reveals that the network interpretation should not be solely based on one network measure, but an explorative analysis results need to be considered because it allows exploring the hidden information through the changes in the network structure, topology patterns and nodes’ position.