The University of Southampton
University of Southampton Institutional Repository

Predicting the structure of covert networks using genetic programming, cognitive work analysis and social network analysis

Predicting the structure of covert networks using genetic programming, cognitive work analysis and social network analysis
Predicting the structure of covert networks using genetic programming, cognitive work analysis and social network analysis
A significant challenge in intelligence analysis involves knowing when a social network description is ‘complete’, i.e., when sufficient connections have been found to render the network complete. In this paper, a combination of methods is used to predict covert network structures for specific missions. The intention is to support hypothesis-generation in the Social Network Analysis of covert organisations. The project employs a four phase approach to modelling social networks, working from task descriptions rather than from contacts between individual: phase one involves the collation of intelligence covering types of mission, in terms of actors and goals; phase two involves the building of task models, based on Cognitive Work Analysis, to provide both a process model of the operation and an indication of the constraints under which the operation will be performed; phase three involves the generation of alternative networks using Genetic Programming; phase four involves the analysis of the resulting networks using social network analysis. Subsequent analysis explores the resilience of the networks, in terms of their resistance to losses of agents or tasks. The project demonstrates that it is possible to define a set of structures that can be tackled using different intervention strategies, demonstrates how patterns of social network structures can be predicted on the basis of task knowledge, and how these structures can be used to guide the gathering of intelligence and to define plausible Covert Networks
University of Birmingham
Stanton, Neville A.
351a44ab-09a0-422a-a738-01df1fe0fadd
Baber, C.
e99ff51e-10d2-48b7-8da6-56990b988f8b
Howard, D.
454794f3-3ddc-42ae-8dbe-60e983c2a01f
Hoghton, Rover J.
d3a04f65-98eb-4388-9bd4-1cd5ef5820b8
Stanton, Neville A.
351a44ab-09a0-422a-a738-01df1fe0fadd
Baber, C.
e99ff51e-10d2-48b7-8da6-56990b988f8b
Howard, D.
454794f3-3ddc-42ae-8dbe-60e983c2a01f
Hoghton, Rover J.
d3a04f65-98eb-4388-9bd4-1cd5ef5820b8

Stanton, Neville A., Baber, C., Howard, D. and Hoghton, Rover J. (2009) Predicting the structure of covert networks using genetic programming, cognitive work analysis and social network analysis University of Birmingham

Record type: Monograph (Project Report)

Abstract

A significant challenge in intelligence analysis involves knowing when a social network description is ‘complete’, i.e., when sufficient connections have been found to render the network complete. In this paper, a combination of methods is used to predict covert network structures for specific missions. The intention is to support hypothesis-generation in the Social Network Analysis of covert organisations. The project employs a four phase approach to modelling social networks, working from task descriptions rather than from contacts between individual: phase one involves the collation of intelligence covering types of mission, in terms of actors and goals; phase two involves the building of task models, based on Cognitive Work Analysis, to provide both a process model of the operation and an indication of the constraints under which the operation will be performed; phase three involves the generation of alternative networks using Genetic Programming; phase four involves the analysis of the resulting networks using social network analysis. Subsequent analysis explores the resilience of the networks, in terms of their resistance to losses of agents or tasks. The project demonstrates that it is possible to define a set of structures that can be tackled using different intervention strategies, demonstrates how patterns of social network structures can be predicted on the basis of task knowledge, and how these structures can be used to guide the gathering of intelligence and to define plausible Covert Networks

Text
__soton.ac.uk_ude_personalfiles_users_jr1d11_mydesktop_ePrints_Predicting the structure of covert networks using genetic programming.pdf - Version of Record
Download (1MB)

More information

Published date: October 2009
Organisations: Transportation Group

Identifiers

Local EPrints ID: 370584
URI: http://eprints.soton.ac.uk/id/eprint/370584
PURE UUID: f117de98-ddf9-4924-8173-cb726d2a1eba
ORCID for Neville A. Stanton: ORCID iD orcid.org/0000-0002-8562-3279

Catalogue record

Date deposited: 31 Oct 2014 14:10
Last modified: 15 Mar 2024 03:33

Export record

Contributors

Author: C. Baber
Author: D. Howard
Author: Rover J. Hoghton

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×