Schweinberger, MichaelPetrescu-Prahova, MirunaVu, Duy Quang2017-06-052017-06-052014Schweinberger, Michael, Petrescu-Prahova, Miruna and Vu, Duy Quang. "Disaster response on September 11, 2001 through the lens of statistical network analysis." <i>Social Networks,</i> 37, (2014) Elsevier: 42-55. https://doi.org/10.1016/j.socnet.2013.12.001.https://hdl.handle.net/1911/94799The rescue and relief operations triggered by the September 11, 2001 attacks on the World Trade Center in New York City demanded collaboration among hundreds of organisations. To shed light on the response to the September 11, 2001 attacks and help to plan and prepare the response to future disasters, we study the inter-organisational network that emerged in response to the attacks. Studying the inter-organisational network can help to shed light on (1) whether some organisations dominated the inter-organisational network and facilitated communication and coordination of the disaster response; (2) whether the dominating organisations were supposed to coordinate disaster response or emerged as coordinators in the wake of the disaster; and (3) the degree of network redundancy and sensitivity of the inter-organisational network to disturbances following the initial disaster. We introduce a Bayesian framework which can answer the substantive questions of interest while being as simple and parsimonious as possible. The framework allows organisations to have varying propensities to collaborate, while taking covariates into account, and allows to assess whether the inter-organisational network had network redundancy—in the form of transitivity—by using a test which may be regarded as a Bayesian score test. We discuss implications in terms of disaster management.engThis is an author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by Elsevier.Disaster response on September 11, 2001 through the lens of statistical network analysisJournal articlediscrete exponential familieshierarchical modelsmixture modelsmodel-based clusteringsocial networksstochastic block modelshttps://doi.org/10.1016/j.socnet.2013.12.001