Centrality of a communication network of construction project participants and implications for improved project communication

Abstract

ABSTRACT The purpose of the study is to use a social network analysis of a construction project’s participants to identify key participants using centrality measures and identify communities of participants in the network. This article analyses the communication network of a construction project that consists of 34 participants. Analysis of four centrality measures of the network’s nodes showed that there was a significant information load for a few key participants. The Eigenvector Centrality was chosen as the most appropriate basic measure of centrality because it takes into account the neighbouring nodes’ level of importance. The Louvain clustering method was found to be more effective than the Girvan–Newman method. The Louvain algorithm divided the project communication network into three communities, in which the participants are interconnected by the technological processes and the work performed. A hypothetical example is presented of how the clustering technique can be used to improve project communication. Adding a ‘Project Manager Assistant’ was selected for addition, and assumptions made to demonstrate how the load might be reduced and effectiveness assessed. These methods of assessing centrality and clustering show potential in project management to analyse a real communication network and when making managerial decisions.