Identifying major civil engineering research influencers and topics using social network analysis

Ibukun T Afolabi,Stephen A Adubi,Oluwole A. Odetunmibi,Joke Badejo,Claudio Cameselle

doi:10.1080/23311916.2020.1835147

Ibukun T Afolabi, Stephen A Adubi + Show 3 more

Open Access

https://doi.org/10.1080/23311916.2020.1835147

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Abstract

This paper focused on applying social network analysis techniques to co-authorship network in order to discover the influencers in Civil engineering research field in Nigeria. It further applies the Latent Dirichlet allocation (LDA) algorithm to uncover the major research topics in this field. The research used 663 publications downloaded from the Scopus database, with the year of publication ranging from 1968 to 2018, using Nigeria as the case study, Civil and Structural engineering as the field of research. The study was carried out using the centrality measures in network analysis such as degree centrality, closeness centrality, and betweenness centrality for co-authorship network analysis of authors and text mining using the LDA algorithm to discover the research focus of the authors. Also, the relationship between the centrality measures and authors’ performance, measured in terms of citation was investigated using regression analysis. The results showed that there was a significantly positive relationship with betweenness centrality and closeness centrality for performance, but a negative relationship with degree centrality. Also the topics discovered using the LDA algorithm helped to reveal the major focus of Civil Engineering research in Nigeria. In conclusion, it is recommended that based on the co-authorship network of civil engineering research in Nigeria, which was found to be a healthy small-world community, the environment discovered can be improved upon to support collaboration and sharing of ideas between researchers in the civil engineering field.

Highlights

Civil Engineering is the branch of engineering that deals with planning, design, and construction of structures for the need of the people (Ricketts et al, 2003) while Structural Engineering is a branch of Civil Engineering that deals with the study and application of structural theory to the design, analysis and evaluation of civil engineering structures (Chen & Liew, 2002)
This paper focused on applying social network analysis techniques to coauthorship network in order to discover the influencers in Civil engineering research field in Nigeria
Several studies have been carried out on the centrality measures of a co-authorship network and these include (Arif et al, 2012; Badar et al, 2013). Some researches such as Abbasi et al (2011) investigated and identified positive influences of centrality on performance out comes in co-authorship network. We explore questions such as “Who are the major influencers of the civil engineering community in Nigeria?”, Are there correlations between performance measures in terms of citation and centrality measures, i.e., does the importance or influence of the researchers correlate with their output in terms of citation

Summary

Introduction

Civil Engineering is the branch of engineering that deals with planning, design, and construction of structures for the need of the people (Ricketts et al, 2003) while Structural Engineering is a branch of Civil Engineering that deals with the study and application of structural theory to the design, analysis and evaluation of civil engineering structures (Chen & Liew, 2002). The national research assessment exercise of Italian universities (a peer review exercise) was used as the major source of data gathering and most of the articles were indexed in the Web of Science database They concluded that in terms of impact (citation metrics), in-house papers garnered less citations than those ones with contributions from external colla borators (collaborators from other universities). Data gathering was done with the Web of Science database They concluded that the co-authorship network consists of 78 authors with a particular researcher leading in all measures of centrality (degree centrality, betweenness centrality, and closeness centrality) and that the collaboration with international colleagues is mostly done with researchers in England and the United States of America. The author identity number was used as nodes for the network construction to avoid name variation of the authors

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