Category Archives: nick christakis

The Friendship Paradox: using social networks to predict spread of epidemics

Nick Christakis and James Fowler (whose research we’ve previously highlighted) is back with research that shows how one can easily use “sensors” in a network to track and get early warning regarding the spread of epidemics.

They took advantage of the “friendship paradox” to do so.  In any real-life network, our friends are more popular than we are.  [This is true mathematically in any group with some loners and some social butterflies.  If you poll members in the group about their friendships, far more of those friends who are reported are going to be the social butterflies.  If far more people reported friendships with the loners, they wouldn’t be loners.  See discussion here.]

Thus by asking random people in a network, in this case Harvard students, about their friends, researchers know that their friends are more centrally located in these networks.    Then one can track behavior among the random group and their friends, in this case the spread of H1N1 flu (swine flu) among 744 Harvard students in 2009.

Those more central in these networks (the “friend” group) got the flu a full 16-47 days earlier than the random group.  Thus, for public authorities, monitoring such a “friend” group could give one early indication of a spreading epidemic; they could serve as “canaries in the coal mine”.  If the process of spreading was person-to-person rather than being exposed to some impersonal information (via a website or a broadcast), one could also track the difference between a random group and a friend group to predict other more positive epidemics, like the spread of information, or the diffusion of a product, or a social norm.

We write in general on this blog about the positive benefits of social ties (social capital), but Fowler and Christakis’ study also shows you that having friends and being centrally located has its costs: in this case getting the flu faster.  [In some ways, this is analogous to Gladwell’s discussion in the Tipping Point of how Mavens, Connectors and Salesmen may be disproportionately influential in the spread of ideas through networks, although Fowler and Christakis are far more mathematical in identifying who these central folks are.]

The “friends group manifested the flu roughly two weeks prior to the random group using one method of detection, and a full 46 days prior to the epidemic peak using another method.

‘We think this may have significant implications for public health,’ said Christakis. ‘Public health officials often track epidemics by following random samples of people or monitoring people after they get sick. But that approach only provides a snapshot of what’s currently happening. By simply asking members of the random group to name friends, and then tracking and comparing both groups, we can predict epidemics before they strike the population at large. This would allow an earlier, more vigorous, and more effective response.’

‘If you want a crystal ball for finding out which parts of the country are going to get the flu first, then this may be the most effective method we have now,’ said Fowler. ‘Currently used methods are based on statistics that lag the real world – or, at best, are contemporaneous with it. We show a way you can get ahead of an epidemic of flu, or potentially anything else that spreads in networks.’

Christakis also notes that if you provided a random 30% in a population with immunity to a flu, you don’t protect the greater public, but if you took a random 30% of the population, asked them to name their friends, and then provided immunization to their friends, in a typical network the “friend” immunization strategy would achieve as high immunity protection for the entire network as giving 96% of the population immunity shots, but at less than 1/3 the cost.

The following video shows how the nodes that light up first (markers for getting the flu) are more central and far less likely to be at the periphery of the social network.  The red dots are people getting the flu; the yellow dots are friends of people with the flu and the size of the dot is proportional to how many of their friends have the flu.

Good summary of this research and its implications here: Nick Christakis TED talk (June 2010) – How social networks predict spread of flu.  Nick also discusses some of the implications of computational social science, which we’ve previously discussed here under the heading of digital traces.  Nick discusses how one could use data gathered from these networks (either passively or actively) to do things like predict recessions from patterns of fuel consumption by truckers, to communicate with drivers of a road of impending traffic jams ahead of them (by monitoring from cell phone users on the road ahead of them how rapidly they are changing cell phone towers) to asking those central in a mobile cellphone network (easily mapable today) to text their daily temperature (to monitor for impending flu epidemics).  Obviously these raise issues of privacy, which Nick does not discuss.

News release of study

Academic article in PLoS ONE

James Fowler on The Colbert Report discussing the book by Fowler and Christakis called Connected.


Nick Christakis presenting a talk at TED — The Hidden Influence of Social Networks. (February 2010).  In the talk he notes that while almost half of the variation in our number of friends is genetically-based (46%), that another equally large portion (47%) of whether your friends know each other is a function of whether your friends are the type that introduce (“knit”) their friends together or keep them apart (what they call “transitivity”).  About a third of whether you are in the center of social networks or not is genetically inherited.  Christakis believes that these social networks are critically important to transmitting ideas, and kindness, and information and goodness; and if society realized how valuable these networks were, we’d focus far more of our time, energy and resources into helping these networks to flourish.

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Loneliness contagious? An oxymoron? (UPDATED 5/2013)

It seems contradictory.  How can the lonely (who are largely outside of social networks) get their loneliness through social networks?

It makes sense when you think of people’s movements in social networks over some period of time.  The lonely may not always have been lonely, but gradually, they tend to cluster together on the periphery of social networks, suggesting that the social connections with other lonely people exacerbates any de facto loneliness they experience.  That the lonely would be somewhere on the periphery of social networks is somewhat tautological, but that the lonely would cluster together at the periphery is not, and is surprising.

The study is by well-respected researchers (psychologist John Cacioppo from U. Chicago, Nick Christakis from Harvard’s School of Public Health, and James Fowler, a political scientist at UCSD) and will appear  in the December issue of the Journal of Personality and Social Psychology.

The Washington Post notes:

Although the study did not examine how loneliness spreads, Cacioppo said other research has provided clues. People who feel lonely tend to act in negative ways toward those they do have contact with, perpetuating the behavior and the emotion, he said.

“Let’s say for whatever reason — the loss of a spouse, a divorce — you get lonely. You then interact with other people in a more negative fashion. That puts them in a negative mood and makes them more likely to interact with other people in a negative fashion and they minimize their social ties and become lonely,” Cacioppo said.

[The research comprising almost 5,000 people  interviewed every two years between 1991 and 2001] showed that having a social connection to a lonely person increased the chances of developing feelings of loneliness. A friend of a lonely person was 52 percent more likely to develop feelings of loneliness by the time of the next interview, the analysis showed. A friend of that person was 25 percent more likely, and a friend of a friend of a friend was 15 percent more likely.The effect was most powerful for a friend, followed by a neighbor, and was much weaker on spouses and siblings, the researchers found. Loneliness spread more easily among women than men, perhaps because women were more likely to articulate emotions, Cacioppo said.

The researchers said the effect could not be the result of lonely people being more likely to associate with other lonely people because they showed the effect over time. “It’s not a birds-of-a-feather-flock-together effect,” Christakis said.

See “Feeling Lonely?  Chances Are You’re Not Alone” (Washington Post, Rob Stein, 12/1/09)

See also blog posts on the contagion effect of happiness, smoking, and obesity.

See article “The Science of Loneliness” (New Republic, Judith Shulevitz, May 2013)

Our genes influence our social networks

Chromosomes magnified - photo by BlueSunFlower

Chromosomes magnified - photo by BlueSunFlower

If you don’t have enough friends or aren’t the social butterfly of your class, now you can blame your genes.

Nick Christakis (Harvard Medical School) and James Fowler (UCSD political scientist) are back with more controversial findings suggesting some genetic determination in our social networks (both in forming friendships and determining where we are in social networks).  Christakis: “the beautiful and complicated pattern of human connection depends on our genes to a significant measure.”  Previous work by Christakis looked at how our social networks and who is in them shape our likelihood of obesity, happiness, and smoking, among other outcomes.

They researched 1,100 same-sex twins in the National Longitudinal Study of Adolescent Health (colloquially called “Add Health”). Add Health examined high school students in 1994-1995 and asked questions regarding economics, physical health and social involvement. Christakis and Fowler compared the social networks and patterns of identical same-sex twins against fraternal ones to separate nature (genes) from nurture (upbringing).

Their findings go far beyond what people might think about the genetic influence on personality traits (being outgoing, shy, etc.). For example, how often the subject was named as a friend and the likelihood that the subject’s friends knew one another were strongly genetically influenced, but interestingly not the number of friends that the subject listed. This suggests a genetic determinant of being popular (beyond a simple disposition toward being outgoing); further buttressing this interpretation, whether the subject was more the center of attention (central to these networks) or more of a social outcast (peripheral to these networks) was also heritable.

Christakis admits that some of the findings are puzzling, like the fact that the likelihood that my friends Bill and John know each other is attributable to my genes; what this likely means is that some people are genetically disposed to introduce their friends to each other more or to host or arrange social events where these friends would have chances to meet each other.

‘Given that social networks play important roles in determining a wide variety of things ranging from employment and wages to the spread of disease, it is important to understand why networks exhibit the patterns that they do,’  Matthew Jackson, a Stanford University economist, wrote in a commentary accompanying the study called “Do We Inherit Our Positions in Life?”.

James Fowler… said its implications go beyond the theoretical. For some time, scientists have suspected a genetic role in certain conditions, such as obesity. Now, Mr. Fowler wants to investigate whether the dynamics of social networks might affect public-health outcomes, for instance, by exposing people to certain behaviors, such as smoking.”

“Our work shows how humans, like ants, may assemble themselves into a ‘super-organism’ with rules governing the assembly, rules that we carry with us deep in our genes,” says Nicholas Christakis.  Christakis et al. also believe that there may be an evolutionary explanation for their findings since one’s position in social networks had costs or benefits to the survival of one’s genes. Being central to a group likely contributed to survival during periods of food scarcity since one could learn where food supplies were, while being peripheral to groups helped genes survive in periods where deadly germs were being transmitted by social contact. Christakis: “It may be that natural selection is acting on not just things like whether or not we can resist the common cold, but also who it is that we are going to come into contact with.”  The paper notes: “There may be many reasons for genetic variation in the ability to attract or the desire to introduce friends.  More friends may mean greater social support in some settings or greater conflict in others.  Having denser social connections may improve groupsolidarity, but it might also insulate a group from beneficial influence or information from individuals outside the group.”  The authors note that more work is required to understand what specific genes are at work and what possible mediating mechanisms might be.

The authors acknowledge some controversy in studies comparing identical twin studies to fraternal twins, with critics noting that identical twins may have a stronger affiliation with  each other that causes them to be more influenced by each other than fraternal twins.  The authors note that twin studies have been validated by comparing identical twins raised apart versus together (suggesting that it is not the shared environment).  The authors further note that personality and cognitive differences between identical and fraternal twins persist even among twins mistakenly believed to be identical by their parents (indicating that parental patterns in raising these ‘identical twins’ can’t explain the outcome).  Finally, they note that that once twins reach adulthood, identical twins living apart tend to become more similar with age, which doesn’t fit with a notion of the importance of their shared environment.

The study appeared online in James Fowler, Christopher Dawes and Nicholas Christakis,  “Model of Genetic Variation in Human Social Networks” in Proceedings of the National Academy of Sciences journal (January 26, 2009).

“More specifically, the results show that genetic factors account for 46% [95% confidence interval 23%, 69%] of the variation in in-degree (how many times a person is named as a friend), but heritability of out-degree (how many friends a person names) is not significant (22%, CI 0%, 47%). In addition, node transitivity [the likelihood that two of a person’s contacts are connected to each other] is significantly heritable, with 47% (CI 13%, 65%) of the variation explained by differences in genes. We also find that genetic variation contributes to variation in other network characteristics; for example, bertween-ness centrality [the fraction of paths through the networks that pass through a given node] is significantly heritable (29%, CI 5%, 39%).”

See also “Genes and the Friends You Make” (Wall Street Journal, 1/27/09 by Philip Shishkin)

See other articles by Christakis et. al on social networks.