A Graph Theory Based Systematic Literature Network Analysis

Abstract

Reviewing the existing literature is the preliminary stage of any research work. In the recent times, researchers have enormous sources to gather literature data related to their research topics, particularly from online journals, directories, and databases. The online sources such as Scopus, Google Scholar, and Web of Science facilitate the researchers to know the updates and current state of the research domains. In traditional methods, a researcher had to collect the related research works, review them, code the information and present them in a narrative manner to specify the research gap in the existing studies. Presentation of a review of earlier studies is not a mere summary of description of earlier studies; it provides critical arguments on hypotheses to be considered and suitable methodology to investigate the topic, list of variables to be investigated, and so on. However, if one considers a huge volume of earlier studies, consolidating the information available in them is not an easy task. Critically exploring the hidden information and patterns in the existing studies, developing a visual/graphical representation of information from the data, and summarizing information through suitable metrics are gray areas in reviewing the existing studies. To overcome these issues, the study attempts to use principles from Graph Theory and proposes a new methodological approach to do the review of literature. Domains such as Sociology and Psychology have recognized the usefulness of Graph Theory, a branch of Mathematics and applied the principles to social network analysis (SNA). SNA adapts metrics such as degree centrality, closeness centrality, betweenness centrality, eigenvector centrality, cluster analysis, and modularity to identify the influential actors (nodes)/persons in the social networks. In this paper, these SNA metrics are compared with analyzing literature data to identify the influential variables in the literature, relationships among variables, and strength of relationships to develop suitable research problems, prioritizing the research problem, identification of variables for the study and to develop hypotheses. The sample literature articles are organized in a structured data and the structured data are visualized through a network graph. Furthermore, the network graph is analyzed by graph visualization and manipulation tools such as Gephi, UCINET, Graphviz, and NodeXL. Gephi 0.9 is used for network graph analysis and the graph theory metrics are investigated for the collected literature data.