Literature DB >> 24707073

Disaster Response on September 11, 2001 Through the Lens of Statistical Network Analysis.

Michael Schweinberger1, Miruna Petrescu-Prahova2, Duy Quang Vu3.   

Abstract

The 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.

Entities:  

Keywords:  discrete exponential families; hierarchical models; mixture models; model-based clustering; social networks; stochastic block models

Year:  2014        PMID: 24707073      PMCID: PMC3973036          DOI: 10.1016/j.socnet.2013.12.001

Source DB:  PubMed          Journal:  Soc Networks        ISSN: 0378-8733


  10 in total

1.  Emergence of scaling in random networks

Authors: 
Journal:  Science       Date:  1999-10-15       Impact factor: 47.728

2.  Random graphs with arbitrary degree distributions and their applications.

Authors:  M E Newman; S H Strogatz; D J Watts
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2001-07-24

Review 3.  Community structure in social and biological networks.

Authors:  M Girvan; M E J Newman
Journal:  Proc Natl Acad Sci U S A       Date:  2002-06-11       Impact factor: 11.205

4.  Likelihood-based inference for stochastic models of sexual network formation.

Authors:  Mark S Handcock; James Holland Jones
Journal:  Theor Popul Biol       Date:  2004-06       Impact factor: 1.570

5.  Curved Exponential Family Models for Social Networks.

Authors:  David R Hunter
Journal:  Soc Networks       Date:  2007-03

6.  ergm: A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks.

Authors:  David R Hunter; Mark S Handcock; Carter T Butts; Steven M Goodreau; Martina Morris
Journal:  J Stat Softw       Date:  2008-05-01       Impact factor: 6.440

7.  A nonparametric view of network models and Newman-Girvan and other modularities.

Authors:  Peter J Bickel; Aiyou Chen
Journal:  Proc Natl Acad Sci U S A       Date:  2009-11-23       Impact factor: 11.205

8.  Mixed Membership Stochastic Blockmodels.

Authors:  Edoardo M Airoldi; David M Blei; Stephen E Fienberg; Eric P Xing
Journal:  J Mach Learn Res       Date:  2008-09       Impact factor: 3.654

9.  Representing Degree Distributions, Clustering, and Homophily in Social Networks With Latent Cluster Random Effects Models.

Authors:  Pavel N Krivitsky; Mark S Handcock; Adrian E Raftery; Peter D Hoff
Journal:  Soc Networks       Date:  2009-07-01

10.  Instability, Sensitivity, and Degeneracy of Discrete Exponential Families.

Authors:  Michael Schweinberger
Journal:  J Am Stat Assoc       Date:  2012-01-24       Impact factor: 5.033

  10 in total
  4 in total

1.  Computational Statistical Methods for Social Network Models.

Authors:  David R Hunter; Pavel N Krivitsky; Michael Schweinberger
Journal:  J Comput Graph Stat       Date:  2012-12-01       Impact factor: 2.302

2.  MODEL-BASED CLUSTERING OF LARGE NETWORKS.

Authors:  Duy Q Vu; David R Hunter; Michael Schweinberger
Journal:  Ann Appl Stat       Date:  2013-12-10       Impact factor: 2.083

3.  Modeling of inter-organizational coordination dynamics in resilience planning of infrastructure systems: A multilayer network simulation framework.

Authors:  Qingchun Li; Shangjia Dong; Ali Mostafavi
Journal:  PLoS One       Date:  2019-11-13       Impact factor: 3.240

4.  Network Hamiltonian models reveal pathways to amyloid fibril formation.

Authors:  Yue Yu; Gianmarc Grazioli; Megha H Unhelkar; Rachel W Martin; Carter T Butts
Journal:  Sci Rep       Date:  2020-09-24       Impact factor: 4.379

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.