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 Hernan A. Makse - Shlomo Havlin

Obesity epidemic (?)

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

Obesity in USA increases with time

What we know on obesity ‘spreading’ 1.Genetics 2.Peer pressure (Christakis and Fowler, NEJM, 2007) 3.Spatial clustering

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

Obesity prevalence in USA

Percolation transition

Time evolution of obesity clusters County obesity %

Largest clusters County obesity %

Neighbors influence (after Christakis, Fowler)

Distance-based correlations

The increase rate is also correlated

Spatial correlations: 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:

Limits High correlations: No correlations:  =0,  =0  =0.5,  =2 (in 2d)

Spatial correlations (constant in time)  =0.5 Obesity  =1.0 Population

Digestive cancer mortality (Changes with time)

Time evolution of 

Phase diagram Uncorrelated Random walk Human activity Economy City growth Population Mortality Cancer mortality Obesity Diabetes Inactivity Lung cancer  / d 

Conclusions Strong spatial correlations in 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