Research contributions published on betweenness centrality algorithm: modelling to analysis in the context of social networking

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.