TITLE:
A Graph Theory Based Systematic Literature Network Analysis
AUTHORS:
Murugaiyan Pachayappan, Ramakrishnan Venkatesakumar
KEYWORDS:
Graph Theory Metrics, Systematic Literature Review, Centrality Measures, Social Network Analysis
JOURNAL NAME:
Theoretical Economics Letters,
Vol.8 No.5,
April
13,
2018
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.