Research contributions published on betweenness centrality algorithm: modelling to analysis in the context of social networking. (5th March 2020)

• Record Type: Journal Article
• Authors: De, Sagar S.; Dehuri, Satchidananda; Cho, Sung-Bae
• Is Part Of: International journal of social network mining. Volume 3:Number 1(2020)
• Page Start: 1
• Page End: 34
• Publication Date: 2020-03-05
• Subjects: betweenness centrality -- bibliographic data collection -- document labelling -- network modelling -- network analysis methods -- preferential attachment -- two-mode analysis -- cluster analysis; Online social networks -- Periodicals; Social networks -- Computer network resources -- Periodicals; Data mining -- Periodicals; 302.30285
• Languages: English
• ISSNs: 1757-8485

Abstract

Social network analysis has become an inevitable tool for the prosperity of modern civilisation. The process of accumulating relational information from structured/unstructured sources, modelling networks, and extracting actionable information requires expertising in several knowledge domains. This paper presents an approach for the analysis of documents in the context of social networking. The approach is illustrated by using a case study related to research contributions published on betweenness centrality algorithm. Distinct networks in terms of article, article-author, and author are modelled and analysed to understand the insights. Consequently, it is possible to identify crucial articles, active authors, groups along with their expertise, research directions, the correlation among documents, and many more. Thus the paper conferred techniques for document collection, pre-processing, network modelling, and network analysis methods for the directed, undirected, weighted, unweighted, connected, disconnected, and bipartite networks.