In an article published last summer in the New England Journal of Medicine, Christakis et al investigate the spread of obesity as a social contagion. Using the dataset from the Framingham Heart Study, they performed a longitudinal study (1971-2003) to study how social networks contribute to individual’s body mass index, or BMI. The researchers discovered multiple clusters of obese persons in the community, and that these clusters were separated by about 3 degrees of separation. A person’s risk of becoming obese increased significantly when he or she had a friend, a spouse, or a sibling who became obese. There was no discernible effect from geographic neighbors, which suggests that physical environment (e.g. sidewalks, parks, crime in one’s neighborhood) may mean much less to the development of obesity than who you associate with.

The spread of obesity through these networks suggests that social norms matter. Individuals may be sensitive to eating and exercising habits of their closest friends, and mimic their behaviors. Malcolm Gladwell argues in the Tipping Point that humans are highly sensitive to the context of their surroundings. Obesity as a social contagion may spread similarly through networks as contagia of fashion or crime.

The accompanying animation illustrates several important points that we’ve covered in class:

1. It shows a large component with several unconnected graphs changing dynamically over time.
2. It demonstrates triatic closure, as obese individuals become more tightly connected with one another. The network becomes more dense over time.
3. It illustrates the concepts of nodes, bridges, edges and the fact that some individuals are more connected than others.