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Network Analysis of the local Network Analysis of the local Public Health Sector: Public Health Sector:
Translating evidence into practiceTranslating evidence into practice
Helen McAneneyHelen McAneney
School of Medicine, Dentistry and Biomedical Sciences,School of Medicine, Dentistry and Biomedical Sciences,Queen’s University BelfastQueen’s University Belfast
Early beginnings for Social Network Analysis
• Stanley Milgram and six
degrees of separation
– the Erdös number and
the Kevin Bacon game
• Granovetter (1973):
– “The strength of weak
ties”
• Watts and Strogatz (1998):
– “Collective dynamics of
small-world networks”Euler’s Konigsberg's Bridges Problem (1736)
Applications
• Knowledge transfer
• Disease transfer
– STDs
– Avian flu (hub airports)
• Drugs/smoking/obesity
• Web, Google
• Citations of articles
• Neighbourhood effects
The shape of the US purely from the flight paths.
SNA Theory
• Nodes (actors) and edges (ties)
• Adjacency matrix A
• SNA measures
– Centrality, centralisation, block-modelling
• Freeman Degree Centrality
– No. of edges attached to it
– Normalised Degree
n
jiji Ak
1
maxkki
SNA Theory
• Bonacich Eigenvector Centrality
– Edges weighted by influence of node connected to
– is largest e-value, x is e-vector of A
• Betweenness Centrality
– Fraction of geodesic paths that a given node lies on
– Control a node has over flow of information
n
jjiji xAx
1
1
A few examples: Star network
• Star network
• Adjacency matrix of
0000001
0000001
0000001
0000001
0000001
0000001
1111110
STARA
A few examples: Star network
• Centrality measures
– Freeman Degree
– Bonacich Eigenvector
– Betweenness
• Centralisation 100%, node1 dominates
Node Degree Eigenvector Betweenness 1 6 0.707 15 2 1 0.29 0 3 1 0.29 0 4 1 0.29 0 5 1 0.29 0 6 1 0.29 0 7 1 0.29 0
A few examples: Circle network
• Circle network
• Adjacency matrix of
0100001
1010000
0101000
0010100
0001010
0000101
1000010
CIRCLEA
A few examples: Circle network
• Centrality measures
– Freeman Degree
– Bonacich Eigenvector
– Betweenness
• Centralisation 0%, all nodes equal
Node Degree Eigenvector Betweenness 1 2 0.38 3 2 2 0.38 3 3 2 0.38 3 4 2 0.38 3 5 2 0.38 3 6 2 0.38 3 7 2 0.38 3
A few examples: Line network
• Line network (‘broken circle’)
• Adjacency matrix of
0010000
0001000
1000100
0100010
0010001
0001001
0000110
LINEA
A few examples: Line network
• Centrality measures
• Centralisation
– 6.67% (degree)
– 39% (e-vector)
– 31% (betweenness)
Node Degree Eigenvector Betweenness 1 2 0.50 9 2 2 0.46 8 3 2 0.46 8 4 2 0.35 5 5 2 0.35 5 6 1 0.19 0 7 1 0.19 0
CoE Network in Public Health
• Launch of UKCRC CoE in
Public Health (NI) June 2008
• Questionnaire to provide
baseline data
• Create a map of PH community
in NI
• 98 participants from 44
organisations & research
clusters
• 193 nodes (organisations)
nominated
How personal goals related to those of CoE
CoE Network in Public Health
193 organisations and research clusters
• Centrality measures
• Centralisation
– 16% (out-degree) & 5% (in-degree)
– 51% (eigenvector)
– 4% (betweenness)
Out-Degree In-Degree Eigenvector Betweenness 1. QUB_CCPS DHSSPS BHSCT DHSSPS 2. EHSSB BHSCT DHSSPS BHSCT 3. NICR IPH QUB_CCPS QUB_NM 4. DHSSPS HSCT UU UU 5. QUB_NM QUB EHSSB IPH 6. BHSCT UU RDO RDO
Block-model of Network
Block-model of Network
Root mean square of impact and strength
Values of 1 (high) – 3 (low)Strongest if 2 (1+1), weakest if 6 (3+3)
Questions for the future
• Identified difference in attitudes/goals of academics & non-academics.
• Sectors with little or no interaction
• Influential organisation
– good or bad?
• ‘Value’ of trans-disciplinary interaction
• CoE’s translational message,
– improving cross collaboration
– improving effectiveness for clinical or PH outcomes
Acknowledgement
• Dr Jim McCann
– School of Mathematics and Physics
• Prof. Lindsay Prior
– School of Sociology, Social Policy and Social Work,
• Jane Wilde CBE
– The Institute of Public Health in Ireland
• Prof. Frank Kee
– Director UKCRC Centre of Excellence for Public Health
– www.qub.ac.uk/coe
