1. Network Analysis of the local Public Health Sector: Translating evidence into practice Helen McAneney School of Medicine, Dentistry and Biomedical Sciences, Queen’s University Belfast

2. Outline Background – Historical setting and recent research Some theory – Centrality, centralisation, block-modelling, A few simple examples – Star, circle and line networks The UKCRC Centre of Excellence network – Results and discussion Questions for the future

3. 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)

4. Applications Knowledge transfer Disease transfer –STDs –Avian flu (hub airports) Drugs/smoking/obesity Web, Google Citations of articles Neighbourhood effects

5. The shape of the US purely from the flight paths.

7. 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

8. 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

9. A few examples: Star network Star network Adjacency matrix of

10. A few examples: Star network Centrality measures –Freeman Degree –Bonacich Eigenvector –Betweenness Centralisation 100%, node1 dominates

11. A few examples: Circle network Circle network Adjacency matrix of

12. A few examples: Circle network Centrality measures –Freeman Degree –Bonacich Eigenvector –Betweenness Centralisation 0%, all nodes equal

13. A few examples: Line network Line network (‘broken circle’) Adjacency matrix of

14. A few examples: Line network Centrality measures Centralisation –6.67% (degree) –39% (e-vector) –31% (betweenness)

15. 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

16. How personal goals related to those of CoE

17. CoE Network in Public Health 193 organisations and research clusters

18. Centrality measures Centralisation –16% (out-degree) & 5% (in-degree) –51% (eigenvector) –4% (betweenness)

19. Block-model of Network

20. Root mean square of impact and strength Values of 1 (high) – 3 (low) Strongest if 2 (1+1), weakest if 6 (3+3)

21. 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

22. 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