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“ You must be the change you wish to see in the World.” Mahatma Gandhi Connected
Connected
Obesity Clusters FHS NETWORK Connected
Three Degrees Of Influence FHS NETWORK Connected
HOMOPHILY Causes of Similarity and Clustering INFLUENCE CONTEXT Connected
The Spread Of Obesity The Human Superorganism FROM 1971 TO 2003
Spread of Obesity Connected NA Christakis and JH Fowler, “The Spread of Obesity in a Large Social Network Over 32 Years,”  New England Journal of Medicine   2007; 357: 370-379 Ego-Perceived Friend Mutual Friend Alter-Perceived Friend Same Sex Friend Opposite Sex Friend Spouse Sibling Same Sex Sibling Opposite Sex Sibling Immediate Neighbor Small Workplace Co-worker 0 100 200 300 PERCENTAGE INCREASE IN RISK OF OBESITY SOCIAL CONTACT
2001 1971 Smoking Clusters FHS NETWORK Connected
Drinking Clusters FHS NETWORK Connected
Happiness Clusters FHS NETWORK Connected
Clusters Sleep ADDHEALTH NETWORK Connected
Generosity Cascades EXPERIMENTAL NETWORK Connected
Online Networks REAL FRIENDS Connected K Lewis, J Kaufman, M Gonzalez, A Wimmer, and NA Christakis, “Tastes, Ties, and Time,”  Social Networks   2008; 30: 330-342
Online Networks FRIENDS AND CLUBS Connected K Lewis, J Kaufman, M Gonzalez, A Wimmer, and NA Christakis, “Tastes, Ties, and Time,”  Social Networks   2008; 30: 330-342
Connected Online Networks FRIENDS, CLUBS,  AND ROOMMATES K Lewis, J Kaufman, M Gonzalez, A Wimmer, and NA Christakis, “Tastes, Ties, and Time,”  Social Networks   2008; 30: 330-342
Online Networks FRIENDS, CLUBS,  Connected ROOMMATES, FACEBOOK K Lewis, J Kaufman, M Gonzalez, A Wimmer, and NA Christakis, “Tastes, Ties, and Time,”  Social Networks   2008; 30: 330-342
Smiling Clusters FACEBOOK NETWORK Connected Smilers Non Smilers
Obesity Clusters FACEBOOK NETWORK Connected
Movie Clusters source: www.ConnectedTheBook.com FACEBOOK NETWORK Connected
What Happened to Free Will?!? Connected
Connected
Don’t Dump Your Friends! Connected
Connected
Get Your Friends Involved Connected
Connected
Connected Realize Your Own Network Power
“ You must be the change you wish to see in your  Network .” Connected

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TEDxBayArea July event

Notes de l'éditeur

  1. “ Social networks are intricate things of beauty. They are so elaborate and so complex — and so ubiquitous , in fact — that one has to wonder what purpose they serve. Why are we embedded in them? How do they form? How do they work? How do they affect us?”
  2. What might cause this clustering? REVIEW All three are typically present , and it takes effort to tease them apart. We used a variety of strategies to sort this out.
  3. With this kind of perspective, I came to see things differently. This network moves. Things flow in it. It changes and evolves. It is resilient to injury. It has a memory. It has a coherence and endurance across time . And so I came to see social networks as living things. And, we used a variety of techniques to study these living networks, to put them under the microscope. For instance one of our tricks was to exploit the directionality of friendship . ASK: ego/alter pairs.
  4. The graphic shows the percentage increase in ego’s risk of obesity given that alter becomes obese. So, a friend becoming obese increases an ego’s risk of being obese. This holds if ego nominates alter, but not if alter nominated ego. Esteem is important . This directional data is also important because it suggests that confounding by unobserved factors is not the source of the relationship. And, the effect is gendered -- among friends, spouses, and siblings, further supporting the social nature of the effect at hand. many mechanisms: spread of behaviors – muffins and beer or a spread of norms – the idea of acceptable body size Headline writers had a field day. I want to be clear, of course, that we do not think that our work should justify any sort of prejudice . we started exploring all sorts of other phenomena And eventually, we became interested in emotions.
  5. Well, do things spread online too? Does the online world resembles the offline world, and how it is different? Of course, people have multiplex interactions, the nature of which can change with new technology, at least in some ways. Here is a natural social networks of 105 close friends in a dorm. The average number of close friends in this sample is 6.6.
  6. They can also be members of the same ethnically based club.
  7. They can also be roommates
  8. Or they be Facebook Friends, where the average number is 110.