Social Network Analysis as a Tool for Manipulating and Analysing Scattered Data Dr Rhys Williams, Policy and Capability Studies Dept. rlwilliams@dstl.gov.uk

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

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

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

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

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 http://www.orgnet.com 17 January 2019

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

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

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

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

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

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

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

Key Paths of Interest Links inferred by affiliation Reported links C 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

Inter-group Distance Comparison 17 January 2019

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

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

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

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

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

Questions? 17 January 2019