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.

摘要

该研究的目的是对建设项目参与者进行社交网络分析，以使用集中度度量来识别关键参与者，并识别网络中参与者的社区。本文分析了一个由34名参与者组成的建设项目的通信网络。对网络节点的四个集中度度量的分析表明，对于一些关键参与者而言，信息负载很大。特征向量中心性被选为最合适的中心性基本度量，因为它考虑了相邻节点的重要性水平。发现Louvain聚类方法比Girvan-Newman方法更有效。Louvain算法将项目通讯网络分为三个社区，其中参与者通过技术过程和所执行的工作相互联系。给出了一个假设的示例，说明如何使用聚类技术改善项目沟通。选择添加“项目经理助理”进行添加，并进行了假设以演示如何减少负载并评估有效性。这些评估中心性和聚类的方法显示了在项目管理中分析真实的通信网络以及做出管理决策时的潜力。