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Clustering and spreading of behavior and opinion in social networks

Clustering and spreading of behavior and opinion in social networks. Lazaros Gallos Levich Institute, City College of New York Hernan A. Makse - Shlomo Havlin. Clustering and spreading of behavior in social networks. Lazaros Gallos Levich Institute, City College of New York

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Clustering and spreading of behavior and opinion in social networks

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  1. Clustering and spreading of behavior and opinion in social networks LazarosGallos LevichInstitute, City College of New York Hernan A. Makse - ShlomoHavlin

  2. Clustering and spreading of behavior in social networks LazarosGallos LevichInstitute, City College of New York Hernan A. Makse - ShlomoHavlin

  3. Obesity epidemic (?)

  4. BMI and obesity The Body Mass Index (BMI) is a standard measure of human body fat BMI>30 is generally accepted as the obesity threshold

  5. Obesity in USA increases with time

  6. What we know on obesity ‘spreading’ • Genetics • Peer pressure(Christakis and Fowler, NEJM, 2007) • Spatial clustering

  7. Our approach • The physics of clustering is challenging • Study obesity as a percolation process • Use scaling analysis • More properties

  8. Obesity prevalence in USA

  9. Percolation transition

  10. Time evolution of obesity clusters County obesity %

  11. Largest clusters County obesity %

  12. Neighbors influence (after Christakis, Fowler)

  13. Distance-based correlations

  14. The increase rate is also correlated

  15. Scaling theory of Growth • Standard theory of Gibrat assumes random growth • Scaling concepts introduced by the H.E. Stanley group(Stanley, Nature, 1996) for the growth of companies • Extended to more properties (e.g. cities) Growth rate: Spatial correlations:

  16. Limits High correlations: No correlations: b =0, g =0 b =0.5 , g =2 (in 2d)

  17. Spatial correlations (constant in time) g =0.5 Obesity • g =1.0 • Population

  18. Digestive cancer mortality(Changes with time)

  19. Time evolution of g Weak correlations Strong correlations

  20. Phase diagram g /d 1/4 1/2 1 Weak correlations Strong correlations

  21. Conclusions • Strong spatial correlationsin obesity spreading • Obesity clusters grow faster than the population growth • Scaling analysis quantifies the degree of spatial correlations • Exponents are related Three main universality classes based on spatial correlations

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