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

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.