MAPPing Dark Networks : A Data Transformation Strategy for Clandestine Organizations

1. MAPPing Dark Networks:A Data Transformation Strategy for Clandestine Organizations Luke M. Gerdes Minerva Fellow, United States Military Academy Luke.Gerdes@usma.edu A Presentation for the 2013 INSNA Sunbelt Conference Hamburg, Germany 24 May 2013 The views expressed herein are those of the authors and do not purport to represent the official policy or position of the United States Military Academy, the Department of the Army, the Department of Defense, or the U.S. Government.

2. Bottom Line Up Front • Data transformation plays an important role in determining results • Topical context/subject matter must be considered when determining a transformation strategy • Data mis-management can cause sub-optimal performance in influence &/or decapitation campaigns

3. Basic Questions • Why transform? • Necessary to implement standard network measures • Multi-modal measures (e.g. Faust 1997) not widely accepted • Multi-modal measures non-intuitive • How to transform? • Several approaches • Binary folding • One-way sums • Discount by size of partnership (Newman) • Resource flow-based approach (Zhou, et al) • Median Additive Projection Process (MAPP)

4. Binary Folding

5. Weighted Folding: A Bad Idea Weighted Data

6. One-Way Sums

7. Newman’s Method of Discounting

8. Zhou’s Binary Resource Flows

9. A Weighted Generalization of Zhou

10. Assumptions About Dark Networks Dick and Harry’s Participation in Event C • Data is always undirected • Exact timing of interaction is unknown • Maximum possible interactions between two agents equal to larger number of ties to event • Minimum possible interactions between two agents equal to zero

11. Median Additive Projection Process

12. Evaluating Differences • 27 settings • Agents (25, 50, or 100) • Events (5, 10, 0r 20) • Density (.1, .25, or .33) • 5 networks/setting • 135 2-mode networks • 6 transformation processes/ 2-mode network • 810 1-mode networks • 3 measures of centrality / 1-mode network • Opsahl’s 3rd generation measures (alpha = 0.5) • 2430 comparisons of rank • Spearman’s rho • Bonferroni’s correction for multiple comparisons (by setting)

13. Results for 50 Agents: Degree Each star (*) represents a test that was significant at the 0.05 level

14. Results for 50 Agents: Closeness Each star (*) represents a test that was significant at the 0.05 level

15. Results for 50 Agents: Betweenness Each star (*) represents a test that was significant at the 0.05 level

16. . . . And When Considering ‘Top’ Agents (Degree) Each star (*) represents a test that was significant at the 0.05 level

17. Conclusions on Data Transformation • Data transformation is not trivial • Different processes produce different rankings • Different processes select different actors as highly central nodes • Degree more robust than closeness & betweenness • None of the measurements robust in selection of ‘top’ agents • No means to determine best performance • Determination would require comparison against 1-mode network built through direct observation • Selection of method must be rooted in theory • Different methods appropriate to different topics • MAPP the most appropriate to dark networks

18. Policy Implications • Countering dark networks • Projection processes determine decapitation & influence strategies • Conventional wisdom (i.e. binary folding) likely leads to less-effective interventions • Removal of the wrong people • Mis-targeted campaigns to influence ‘key’ opinion-makers