A Causal Model Analysis Approach to Literature Reviewing: A Tutorial on the Application of Graph Theory

28 Pages Posted: 30 Jun 2021

See all articles by Yuanyuan Song

Yuanyuan Song

The University of Georgia

Richard Thomas Watson

Xia Zhao

University of Georgia

Nathaniel Kelley

affiliation not provided to SSRN

Date Written: June 20, 2021

Abstract

Current literature search approaches rely on searching publications with keywords, reading related papers, and manually synthesizing knowledge. This approach is inefficient and limits the pace of scientific progress. To address such issues, we develop a causal model-based approach for literature reviewing. The method focuses on coding the core knowledge in publications (i.e., causal or process models) as directed graphs following a well-defined regimen and combining the directed graphs into a labeled property graph database. We provide guidance on coding publications as graphs using the Cypher programming language. An application is available to facilitate scholars to code publications as a graph database. We discuss the contributions of our method in terms of query-based knowledge extraction and assisting knowledge synthesis using various network analysis metrics.

Keywords: Literature review, Causal model, Graph theory, Cypher query, Knowledge synthesis

Suggested Citation

Song, Yuanyuan and Watson, Richard Thomas and Zhao, Xia and Kelley, Nathaniel, A Causal Model Analysis Approach to Literature Reviewing: A Tutorial on the Application of Graph Theory (June 20, 2021). Available at SSRN: https://ssrn.com/abstract=3870782 or http://dx.doi.org/10.2139/ssrn.3870782

Yuanyuan Song (Contact Author)

The University of Georgia

Athens, GA 30605
United States

Xia Zhao

University of Georgia ( email )

610 S. Lumpkin St.
Benson C404
Athens, GA 30602
United States

Nathaniel Kelley

affiliation not provided to SSRN

No contact information is available for Richard Thomas Watson

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