1. Social Network Analysis as a Tool for Manipulating and Analysing Scattered Data
Dr Rhys Williams,
Policy and Capability Studies Dept.

2. Overview What is Social Network Analysis?
Social Network Analysis Case Study
Probabilistic Inference in Networks
Wider Network Problems in Defence
17 January 2019

3. What is Social Network Analysis?
Social science tool for studying social structure
Mathematical metrics for socio-structural attributes
Traditionally used for social science case studies
Complete data sets
Studies of group interactions, ethnic communities
Generated using i2: Analyst’s Notebook
Case Study Objectives
Choose Appropriate Metrics
Collect Data – Questionnaires/Surveys
17 January 2019

4. SNA Intelligence Case Study
Help analysts understand:
Datasets they have
What they need to collect
No examples of how to deal with intelligence data
How should SNA be applied?
Nodes to represent people, groups, locations
Links to represent professional, supply, friendship relations
Choose right metrics to get useful results
17 January 2019

5. SNA in Defence OA Adversaries are more networked, as are we
We need tools to better understand adversary systems
Networked organisations (e.g. terrorist groups)
Useful for analysing large and inter-linked systems
Can identify key nodes and links
17 January 2019

6. Intelligence Applications
SNA widely mooted as intelligence analysis tool
Academia - M. Sparrow, K. Carley (CASOS), S. Borgatti
Government
Very little work in open fora
Valdis Krebs’ case study of 9-11 hijackers
17 January 2019

7. Intelligence Analysis
Immersion in a subject or geographical area
Huge data sets
Little work on structural analysis
Some departments beginning to use visualisation – i2 Analyst’s Notebook
Generated using i2: Analyst’s Notebook
17 January 2019

8. Choosing Methods MoD asked us: Which SNA methods?
How does group ‘A’ interact with other groups?
Which SNA methods?
Clustering
Density
Centrality measurement
Paths between nodes
17 January 2019

9. Key Nodes Can define nodes to represent people, groups, locations
Centrality measures:
Degree Centrality
Betweenness Centrality
17 January 2019

10. Link Categories and Weights
Relational, professional, virtual, supply
Weights
Intensity of link (e.g. love > friendship > casual association)
Frequency of interactions
Reliability of data
Difficult to find a consistent way of weighting links
17 January 2019

11. Stronger Link Categories
Centrality Ranks with Different Link Categories
All Link Categories
Centrality Score
Stronger Link Categories Al 5
Mike 4
Dave
John 3
Clare 2
Tim 1 Ed
Steve
Sue
Joe
Phil
17 January 2019

12. Centrality Variation Could represent Changing focus of reporting
Improving intelligence picture
Changes within the organisation being observed
17 January 2019

13. Affiliation and Inter-lock Networks
Deduce links between nodes (disks) that share attributes (squares)
Co-participation in events
Attendance of same institutions
Flexible cross-referencing of data
Affiliation links are not definite
17 January 2019

14. Key Paths of Interest Links inferred by affiliation Reported linksC D E F G H I J
Links inferred by affiliation
Inferred links make nodes appear closer, but the links need further examination
i2 software could be used this way – searching through cards
Reported links
17 January 2019

15. Inter-group Distance Comparison
17 January 2019

16. Link Prediction – Bayesian Inference
Techniques used in molecular biology
Start with a ‘Gold Standard’ set of confirmed links
Probability of links existing between nodes with shared affiliations
Case study using open source reported data on Greek November 17 terror group
17 January 2019

17. November 17 Social Links Accurate link prediction
Could be used to prioritise intelligence collection
Generated using Netviz
17 January 2019

18. Benefits Most useful when applied to very large datasets
Showed analysts what data they had
Helped determine what data was needed
Developed a systematic method
Linking databases to software - audit trail
Established categories for nodes and links
Use of particular metrics
17 January 2019

19. Issues Using intelligence data is much more complicated
Can’t use questionnaires!
Incomplete/unreliable data
No ‘even’ datasets - information is focused on individuals of interest
Standard metrics such as density and clustering have little meaning in this context
Network represents agency’s collection, not reality on the ground
Continuous process of coding and updating data
Case studies = best way to learn
17 January 2019

20. Wider Application to Defence OA
Organisational modelling – Blue easier than Red
Communication network models – useful complement to bandwidth analysis
Work using SNA to analyse C4ISR Architectures being done in Australia
Techniques for information propagation and epidemiology
17 January 2019

21. Questions?
17 January 2019