Social Network Analysis as an Intelligence Technique: the Iranian Nuclear Weapons Program Revisited.

Transcription

1 Social Network Analysis as an Intelligence Technique: the Iranian Nuclear Weapons Program Revisited. (Sometimes a Picture is Only Worth a Couple of Words!) by Graham Durant-Law CSC

2 Presentation Outline A Quick Network Analysis Lesson What is the lexicon of network analysis? Typical measures. Network analysis nirvana. A Case Study: Iranian Nuclear Physicists The network. The same network. Discrete network. Concluding Comments 2

3 A Quick Network Analysis Lesson. Science is built with facts, as a house is with stones. But a collection of facts is no more a science than a heap of stones is a house. Henri Poincare, Mathematician, 1901.

4 What is Network Analysis? Network analysis is based on an assumption of the importance of relationships among interacting nodes. A methodology that provides the ability to examine quantitatively, qualitatively, and graphically macro and micro linkages between nodes. A connection between two or more nodes means there is some sort of relationship. Unit of data is the dyad pairs of nodes. 4

5 What is the Lexicon of Network Analysis? A node is the smallest unit in the network. It is also known as a vertex or entity. A tie is a line between two nodes indicating there is a relationship between them. It is also known as an edge or link. A graph is a set of nodes and a set of ties between pairs of nodes. A network consists of a graph and additional information on the nodes or the ties of the graph. It is also known as a map. 5

6 Social and Organisational Network Analysis How work really gets done the formal versus shadow organisation Enterprise Portfolio 1 Portfolio 2 Portfolio 3 Program A Program B Program C Program D Program E How does your organisation really work? What is the gap between the formal and shadow organisation? What is the optimum structure? How do people interact across portfolios, and between programs? Who are the informal leaders? Who must be engaged to effect change? 6

7 Typical Measures Degree (ties or links): in ties and out ties represent the number of connections to and from a node. Density: the percentage of connections that exist out of the total possible that could exist. Distance: degrees of separation or the diameter of a network. Reciprocity: the number of bidirectional links expressed as a percentage. Centrality: the extent to which a network is organised around one or more central nodes. Copyright 2011: HyperEdge Pty Ltd 7

8 Organisation Dynamics Providers and Seekers degree centrality Transmitters and Receivers closeness centrality Reveals how much activity is going on and who are the most active members by counting the number of direct links each person has to others in the network. Does not necessarily describe power or influence. People at the centre of the network: are the connector or hub of the network, may be in an advantaged position in the network. are usually less dependent on other individuals. are often a deal maker or broker. Highlights people with the shortest paths to other people, thus allowing them to directly pass on and receive communications quicker than others in the organisation. Is strongly correlated with organisational influence if the individual is a skilled communicator. These individuals are often network brokers. They are often the pulse-takers of the organisation. 8

9 Organisation Dynamics (continued) Brokers and Gatekeepers betweenness centrality Influencers eigenvector centrality Reveals individuals who: connect disparate groups within the network. hold a favoured or powerful position in the network. have great influence over what is communicated through the network. act as intermediaries Identifies the bridges within the network. They may act as the true gatekeeper deciding what does or does not get passed through the network, or as the third who benefits by passing information to others to secure advantage.. Measures how well connected a person is and how much direct influence they may have over the most active people in the network Measures how close a person is to other highly connected people in terms of the global or overall makeup of the network Is a reasonable measure of network positional advantage and/or perceived power. 9

10 Network Analysis Nirvana Every node is visible. Every node s degree is countable: that is the number and direction of ties. Every tie can be followed from source to destination. Clusters and outliers are identifiable. 10

11 A Case Study: Iranian Nuclear Physicists Simply because your data links people and you can visualise that, it does not mean you have performed network analysis. This is akin to displaying a line plot of some stock's price over a quarter and claiming you have performed statistical analysis all you have done is report data! Drew Conway, Political Scientist,

12 The Network? (network analysis nirvana?) 12

13 Cut-Points (people holding the network together) 13

14 Nodes Sized by Degree (in and out links) 14

15 Nodes Sized by Betweenness (ability to get to others) 15

16 The Same Network! (closer to network analysis nirvana) 16

17 Discrete Network 17

18 Degree Centrality (Providers and Seekers) 18

19 Betweenness Centrality (ability to get to others - brokers and gatekeepers) 19

20 Sattari - Ego Network

21 Concluding Comments The real questions refuse to be placated They are the questions asked most frequently and answered most inadequately, the ones that reveal their true natures slowly, reluctantly, most often against your will. Ingrid Bengis, Author,

22 Some Issues and Cautions Must clearly define the unit of analysis that is what are nodes, what are ties, and what are attributes. Observations are usually regarded as the population of interest rather than a sample of some larger population of possible observations. Must define the population, and then cover the whole population to get meaningful network statistics. The mathematical algorithms in the software treat the data as deterministic. That is, measurements are viewed as an accurate reflection of the real or final or equilibrium state of the network. Clearly they are not! Use the right tool and presentation for the job! Visualisation is not analysis. Seek network analysis nirvana. Above all else you must understand your organisation, the data, the resultant network and the assumptions you are making! 22

23 For more details please visit our website at: Alternatively contact: Graham Durant-Law CSC +61 (0) Lyn Goldsworthy AM + 61 (0) lyn@hyperedge.com.au HyperEdge Pty Ltd Post Office Box 3076 Manuka ACT 2603 Australia