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Milward, H.B. and Raab, J. (2003) Dark Networks as Problems. Paper Presented to the 8th Public Management Research Conference at the School of Policy, Planning and Development at University of Southern California, Los Angeles, 29 September-1 October 2005.

has been cited by the following article:

• TITLE: Using Mathematical Models in Decision Making Methodologies to Find Key Nodes in the Noordin Dark Network

  AUTHORS: William P. Fox, Sean F. Everton

  KEYWORDS: Social Network Analysis, Multi-Attribute Decision Making, Analytical Hierarchy Process (AHP), Decision Criterion, Weighted Criterion, TOPSIS, Node Influence, Sensitivity Analysis, Average Weighted Ranks

  JOURNAL NAME: American Journal of Operations Research, Vol.4 No.4, July 25, 2014

  ABSTRACT: A Dark Network is a network that cannot be accessed through tradition means. Once uncovered, to any degree, dark network analysis can be accomplished using the SNA software. The output of SNA software includes many measures and metrics. For each of these measures and metric, the output in ORA additionally provides the ability to obtain a rank ordering of the nodes in terms of these measures. We might use this information in decision making concerning best methods to disrupt or deceive a given dark network. In the Noordin Dark network, different nodes were identified as key nodes based upon the metric used. Our goal in this paper is to use methodologies to identify the key players or nodes in a Dark Network in a similar manner as we previously proposed in social networks. We apply two multi-attribute decision making methods, a hybrid AHP & TOPSIS and an average weighted ranks scheme, to analyze these outputs to find the most influential nodes as a function of the decision makers’ inputs. We compare these methods by illustration using the Noordin Dark Network with seventy-nine nodes. We discuss sensitivity analysis that is applied to the criteria weights in order to measure the change in the ranking of the nodes.