Abstract
In recent era, networks of data are growing massively and forming a shape of complex structure. Data scientists try to analyze different complex networks and utilize these networks to understand the complex structure of a network in a meaningful way. There is a need to detect and identify such a complex network in order to know how these networks provide communication means while using the complex structure. Social network analysis provides methods to explore and analyze such complex networks using graph theories, network properties and community detection algorithms. In this paper, an analysis of co-authorship network of Public Relation and Public Administration subjects of Microsoft Academic Graph (MAG) is presented, using common centrality measures. The authors belong to different research and academic institutes present all over the world. Cohesive groups of authors have been identified and ranked on the basis of centrality measures, such as betweenness, degree, page rank and closeness. Experimental results show the discovery of authors who are good in specific domain, have a strong field knowledge and maintain collaboration among their peers in the field of Public Relations and Public Administration.
Highlights
Many problems in computational sciences like neuroscience, neuro-informatics, pattern recognition, signal processing and machine learning generate massive amounts of multidimensional data with multiple aspects and high dimensionality
The proposed analysis methodology consists of three steps: First, the data is collected from Microsoft Academic Graph (MAG), in second step, the data is preprocessed and transformed in required form, thirdly, we applied centrality measures and ranked the authors related to each field
Data is collected from Microsoft Academic Graph
Summary
Many problems in computational sciences like neuroscience, neuro-informatics, pattern recognition, signal processing and machine learning generate massive amounts of multidimensional data with multiple aspects and high dimensionality. The real world is full of different kinds of complex networks. The complexity of these networks is rapidly increasing day by day, for the enhancement and advancement in the technology. One prominent example of these type of networks is the network of internet users. The social network analysis has been widely explored to discover relationship patterns or communication patterns among individuals, teams, groups, societies, communication devices and even among organizations. By applying social network analysis techniques we can discover different patterns of collaboration among authors. We can discover most active researcher, who is prominent in the field by applying different measures of social network [5]. Some commonly used centrality measures are: degree centrality [5,7,8,9], closeness centrality [5,7,8,9], betweenness centrality [5,8,22] and PageRank [10,11,12,22]
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