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Transcript of 1 Overview of Social Network Analysis Donna Nystrom Aerospace Data Mining Center...
1
Overview of Social Network Analysis
Donna NystromAerospace Data Mining Center
[email protected](310)336-5303
[email protected](310)336-5303
Outline
• Social Network Analysis (SNA)– Why is it relevant?
– What is it?
– How does it work?
• Example applications– Washington Regional Pawn Data Sharing System
– Florida Department of Corrections
[email protected](310)336-5303
Why is SNA Relevant?
• Adversaries across the spectrum of conflict and crime are using network centric operations– Cheap and easy to move information
– Movement of information is hard to detect
– Conflict is now a game of hide and seek
• Analysts must find adversaries and their activities– Collect far more data than analysts can process
– Vast majority of the data is irrelevant
– Key to finding relevant data is relationships between entities
– Automated filtering and analysis is needed
• SNA can be used to filter and analyze data in the context of relationships between entities
[email protected](310)336-5303
What is SNA?
• The mapping, measuring and analysis of relationships and flows between people, groups, organizations, computers or other entities
• Nodes in the network represent entities while the edges show relationships or flows between the nodes
• Not a fixed methodology, but a collection of strategies for analyzing network data
• Applicable to a wide variety of domains involving complex systems
SNA
[email protected](310)336-5303
How does SNA work?
Source: Stocker, R., Green, D. G., and Newth, D. (2001) Consensus and Cohesion in Simulated Social Networks, Journal of Artificial Societies and Social Simulation, Vol 4, No 4.http://jasss.soc.surrey.ac.uk/4/4/5.html
Fully Connected Regular
Random
• Typically not interested in – Fully connected networks
– Regular networks
– Random networks
[email protected](310)336-5303
How does SNA work?
Source: Stocker, R., Green, D. G., and Newth, D. (2001) Consensus and Cohesion in Simulated Social Networks, Journal of Artificial Societies and Social Simulation, Vol 4, No 4. http://jasss.soc.surrey.ac.uk/4/4/5.html
Number of Links
Nu
mb
er o
f N
od
es
• Scale free networks– Connections are not randomly
distributed
– Connections are not randomly added
– Distribution of node connections follows the Power Law
– Small world theory
• Scale free networks occur in many domains involving complex systems
[email protected](310)336-5303
How does SNA work?
• Roles– Leaders
– Gatekeepers
– Outliers
• Structure– Subgroups
– Bridges
– Holes
• Processes– Flow
– Evolution
– Resilience
Scattered Clusters Hub
Multi-Hub Core/periphery
Source: Krebs, V. And Holley, J. Building Sustainable Communities Through Network Building. http://www.orgnet.com/BuildingNetworks.pdf
[email protected](310)336-5303
How does SNA work?
Boy Tarzan
TambaBuli
Cheetah
Jane
Bonzo
JungleJim George Ursula
Kite Network developed by David Krackhardt
• Degree
• Betweeness
• Closeness
Node Metrics
[email protected](310)336-5303
How does SNA work?
Boy Tarzan
TambaBuli
Cheetah
Jane
Bonzo
JungleJim George Ursula
Kite Network developed by David Krackhardt
• Degree distribution
• Betweeness distribution
• Average path length
Network Metrics
• Clustering coefficient
• Degree
• Betweeness
• Closeness
Node Metrics
[email protected](310)336-5303
Aerospace Data Mining Toolkit
Data
Data
Data
Data
Warehouse
Link Discovery
Analysis
PatternAnalysis
PredictiveAnalysis
Social NetworkAnalysis
Cleansing & Conditioning
Results
Pattern-basedquery
Entity-basedquery
All links query
Iterativequery
Input
[email protected](310)336-5303
Temporal Network
[email protected](310)336-5303
Temporal Network
[email protected](310)336-5303
Summary
• Analysts are overwhelmed with massive amounts of data and automated filtering and analysis is needed.
• SNA can be used to filter and analyze data in the context of relationships between entities.
• Aerospace projects are applying data mining and SNA in novel ways to address the needs of our public safety partners.
• Because of the versatility of SNA, applications to new problems and domains are expected in the future.