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

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Page 1: Clustering and spreading of behavior and opinion in social networks Lazaros Gallos Levich Institute, City College of New York Hernan A. Makse - Shlomo

Clustering and spreading of behavior and opinion in social networksLazaros GallosLevich Institute, City College of New York

Hernan A. Makse - Shlomo Havlin

Page 2: Clustering and spreading of behavior and opinion in social networks Lazaros Gallos Levich Institute, City College of New York Hernan A. Makse - Shlomo

Clustering and spreading of behavior in social networksLazaros GallosLevich Institute, City College of New York

Hernan A. Makse - Shlomo Havlin

Page 3: Clustering and spreading of behavior and opinion in social networks Lazaros Gallos Levich Institute, City College of New York Hernan A. Makse - Shlomo

Obesity epidemic (?)

Page 4: Clustering and spreading of behavior and opinion in social networks Lazaros Gallos Levich Institute, City College of New York Hernan A. Makse - Shlomo

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

Page 5: Clustering and spreading of behavior and opinion in social networks Lazaros Gallos Levich Institute, City College of New York Hernan A. Makse - Shlomo

Obesity in USA increases with time

Page 6: Clustering and spreading of behavior and opinion in social networks Lazaros Gallos Levich Institute, City College of New York Hernan A. Makse - Shlomo

What we know on obesity ‘spreading’

1. Genetics2. Peer pressure

(Christakis and Fowler, NEJM, 2007)3. Spatial clustering

Page 7: Clustering and spreading of behavior and opinion in social networks Lazaros Gallos Levich Institute, City College of New York Hernan A. Makse - Shlomo

Our approach

• The physics of clustering is challenging

• Study obesity as a percolation process

• Use scaling analysis

• More properties

Page 8: Clustering and spreading of behavior and opinion in social networks Lazaros Gallos Levich Institute, City College of New York Hernan A. Makse - Shlomo

Obesity prevalence in USA

Page 9: Clustering and spreading of behavior and opinion in social networks Lazaros Gallos Levich Institute, City College of New York Hernan A. Makse - Shlomo

Percolation transition

Page 10: Clustering and spreading of behavior and opinion in social networks Lazaros Gallos Levich Institute, City College of New York Hernan A. Makse - Shlomo

Time evolution of obesity clusters

County obesity %

Page 11: Clustering and spreading of behavior and opinion in social networks Lazaros Gallos Levich Institute, City College of New York Hernan A. Makse - Shlomo

Largest clusters

County obesity %

Page 12: Clustering and spreading of behavior and opinion in social networks Lazaros Gallos Levich Institute, City College of New York Hernan A. Makse - Shlomo

Neighbors influence

(after Christakis, Fowler)

Page 13: Clustering and spreading of behavior and opinion in social networks Lazaros Gallos Levich Institute, City College of New York Hernan A. Makse - Shlomo

Distance-based correlations

Page 14: Clustering and spreading of behavior and opinion in social networks Lazaros Gallos Levich Institute, City College of New York Hernan A. Makse - Shlomo

The increase rate is also correlated

Page 15: Clustering and spreading of behavior and opinion in social networks Lazaros Gallos Levich Institute, City College of New York Hernan A. Makse - Shlomo

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:

𝛽=𝛾2𝑑

Page 16: Clustering and spreading of behavior and opinion in social networks Lazaros Gallos Levich Institute, City College of New York Hernan A. Makse - Shlomo

Limits

𝛽=𝛾2𝑑

High correlations: No correlations:

b =0, g =0 b =0.5 , g=2 (in 2d)

Page 17: Clustering and spreading of behavior and opinion in social networks Lazaros Gallos Levich Institute, City College of New York Hernan A. Makse - Shlomo

Spatial correlations (constant in time)

g =0.5Obesity

g =1.0Population

Page 18: Clustering and spreading of behavior and opinion in social networks Lazaros Gallos Levich Institute, City College of New York Hernan A. Makse - Shlomo

Digestive cancer mortality(Changes with time)

Page 19: Clustering and spreading of behavior and opinion in social networks Lazaros Gallos Levich Institute, City College of New York Hernan A. Makse - Shlomo

Time evolution of g

Weak correlations

Strong correlations

Page 20: Clustering and spreading of behavior and opinion in social networks Lazaros Gallos Levich Institute, City College of New York Hernan A. Makse - Shlomo

Phase diagram

Uncorrelated

Random walk

Human activity

Economy

City growth

Population

Mortality

Cancer mortality

Obesity

Diabetes

Inactivity

Lung cancer

g /d11/21/4

Weak

correlationsStrong

correlations

Page 21: Clustering and spreading of behavior and opinion in social networks Lazaros Gallos Levich Institute, City College of New York Hernan A. Makse - Shlomo

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