A fuzzy closeness centrality using andness-direction to control degree of closeness

Davidsen, Sören Atmakuri; Padmavathamma, M.

doi:10.1109/cnsc.2014.6906711

Cited by 3 publications

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, closeness centrality measures the centrality not only from the directly linked nodes to a node in a network, but also that of the nodes that are indirectly linked to the node [32]. For this reason, closeness centrality is typically used for measuring how fast information will spread from one node in a network to all other nodes, or, in a network planning situation whose nodes are favorable starting points [33]. Given that construction systems are tightly coupled systems where some events that occurred in one part of the system may cause events in other parts [19], amongst the three fundamental measures of centrality, Closeness is of interest in this paper since it provide means for quantifying an actor's contribution to the global network [6].…”

Section: Social Network Analysismentioning

confidence: 99%

Application of Association Rule Mining and Social Network Analysis for Understanding Causality of Construction Defects

et al. 2019

View full text Add to dashboard Cite

A construction defect can cause schedule delay, cost overrun and quality deterioration. In order to minimize these negative impacts of construction defects, this paper aims to analyze the causality of construction defects. Specifically, association rule mining (ARM) is used to quantify the interrelationships between defect causes, and social network analysis (SNA) is utilized to find out the most influential causes triggering generation of construction defects. The suggested approach was applied to 2949 defect instances in finishing work. Through this application, it was confirmed that the proposed approach can systematically identify and quantify causality among defect causes.

show abstract

Section: Social Network Analysismentioning

confidence: 99%