Disrupting Dark Networks



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Calhoun: The NPS Institutional Archive
Faculty and Researcher Publications
Faculty and Researcher Publications
2008
Tracking, Destabilizing and Disrupting
Dark Networks with Social Networks Analysis
Everton, Sean S.
http://hdl.handle.net/10945/34415



Version 1.05



T
RACKING
,

D
ESTABILIZING AND
D
ISRUPTING
D
ARK
N
ETWORKS WITH

S
OCIAL
N
ETWORKS
A
NALYSIS


Sean F. Everton Naval Postgraduate School



Version 1.05
T
ABLE OF
C
ONTENTS

Chapter 1: Introduction Chapter 2: Social Network Analysis Terms, Concepts and Assumptions …… 10 Chapter 3: Getting Started with UCINET, NetDraw and Pajek………………. 30 Chapter 4: Gathering, Collecting, Manipulating and Visualizing Social Network Data The Basics. 52 Chapter 5: Network Topography ………………..……………………………. 92 Chapter 6: Centrality and Power ……………………………………………... 109 Chapter 7: Cohesion and Clustering …………………………………………. 126 Chapter 8: Brokers, Bridges and Structural Holes …………………………… 154 Chapter 9: Strategic Choices in Disrupting Dark Networks. 178 Appendices …………………………………………………………………..... 187 Appendix 1: Noordin’s Networks …………………………………………187 Appendix 2: Glossary ……………………………………………………. 199 Appendix 3: Multidimensional Scaling Using UCINET …………….…... 202 References. 212


Version 1.05
1



Developing a working hypothesis
Identifying, collecting and recording social network data
Aggregating networks
Analysis: metrics and visualization
Developing strategies and policy recommendations
Social network (and social network analysis)
Figure 2.1: hypothetical social network density
Path (and path distance)
Cohesive subgroups
Interdependence of actors
Ties as channels
Social structure in terms of social networks
Structural location beliefs, norms and behavior
Dynamic social networks
Getting started with ucinet
Getting started with netdraw
Figure 3.7: netdraw’s color lines by relation dialog box mapping algorithms in netdraw
Working with attributes in netdraw
Figure 3.8: netdraw’s color nodes by color dialog box figure 3.9
Getting started with pajek
Mapping algorithms in pajek
Working with attributes in pajek
Realist vs. nominalist strategies
Definitional focus attributes, relations or events
Complete networks
Symmetric one-mode networks
Figure 4.2: subset of padgett and ansell’s marriage data asymmetric one-mode networks
Two-mode networks
Direct observation
Other approaches
One-mode social network data
Two-mode social network data
Deriving one-mode social networks from two-mode social networks
Figure 4.13: noordin attribute data attribute data
Multiple relations in ucinet and netdraw
Figure 4.22: network map of noordin’s aggregated network figure 4.23
Figure 4.25: ucinet dichotomize (binarize) dialog box figure 4.26
Multiple relations in pajek
Figure 4.30: network map of noordin’s aggregated network figure 4.31
Aggregating data in pajek
Positive and negative relations in ucinet and pajek
Figure 4.36: ucinet between dataset statistical summaries dialog box figure 4.37
Weak and strong ties in the new york apparel industry
Network density in ucinet and pajek
Average degree in ucinet and pajek
Weak ties, small worlds and network performance
Estimating clustering coefficients and path distance in ucinet
Estimating clustering coefficients in pajek
Small world q and dark networks
Centrality in ucinet
Centrality in netdraw
Centrality in pajek
Figure 6.9: ucinet’s extract submatrix dialog box next, click on the l
Figure 6.10:
Figure 7.1: simple unconnected directed network identifying components in ucinet
Figure 7.4: ucinet (weak) components output log visualizing components in netdraw
Figure 7.5: netdraw visualization of the attiro network’s strong components figure 7.6
Identifying components in pajek
Identifying cores in ucinet and netdraw
Figure 7.17: ucinet k-core output file (noordin combined network) figure 7.18
Figure 7.23: clique example (see de nooy, mrvar and batagelj 2005:66) identifying cliques in ucinet and netdraw
Figure 7.26: ucinet s extract submatrix dialog box figure 7.27
N-cliques and n-clans
Constraint (structural holes) in ucinet & netdraw
Figure 8.5: netdraw’s size of nodes dialog box figure 8.6
Constraint (structural holes) in pajek
Bridges, bi-components and brokers in netdraw and ucinet
Figure 8.13:
Bridges, bi-components and brokers in pajek
Figure 8.15: pajek hierarchy of bi-components
Figure 8.18:
Figure 8.19: ucinet brokerage role dialog box figure 8.20
Figure 9.2: noordin institutional network (central actors highlighted) figure 9.3
Complete network:
Multidimensional scaling
Simple undirected graph
Metric multidimensional scaling in ucinet
Figure ab metric mds dialog box
Figure ab ucinets metric mds output
Figure ab ucinet non-metric mds scaling dialog box
Figure ab ucinets non-metric mds output
Figure ab ucinet structural equivalence (correlation) matrix



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