Professor Nicholas Christakis is a social scientist and physician who conducts research in the areas of biosocial science, network science, and behaviour genetics. He directs the Human Nature Lab at Yale University, and is the co-director of the Yale Institute for Network Science. He is the Sol Goldman Family professor of Social and Natural Science, appointed in the Departments of Sociology; Medicine; Ecology and Evolutionary Biology; and Biomedical Engineering at Yale University. As of July 2015, he also serves as the master of Silliman College at Yale. His current research focuses on how our biology and health are affected by social interactions and social networks.
We are all connected. Our social networks affect our health and wellbeing, and we can exploit our understanding of social interactions to make the world a better place. Our health depends not only on our own choices and actions, but on the choices and actions of the people around us.
What is a network?
A network comprises a group of individuals who have social ties to each other. It has a specific number and pattern of ties.
There are both artificial and natural networks. Artificial networks have a planned structure and are often used for transmitting information in an organised way – for example, a telephone tree. In contrast, natural networks – real social networks – are elaborate and complex.
How networks affect our health and wellbeing
Obesity provides a case study which demonstrates the structure and function of social networks, and how they affect our lives, our thoughts and our health. In the United States, the prevalence of obesity has increased substantially over the past 30 years. Obesity has been described as an epidemic, and this is true not only in terms of its rapid increase in prevalence but also in the sense of a contagion that can pass from one person to the next. People’s weight is influenced by the weight of the people around them. Social network maps show clusters of obesity – people with obese friends are 45 per cent more likely to be obese themselves. The likelihood of a person being obese is elevated even if friends of their friends are obese, down to three degrees of separation.
All sorts of other phenomena can spread within networks, too. People’s attitudes, decisions, emotions and behaviour depend on those of others, and the extent of influence spreading to three degrees of separation is a common rule of thumb. Networks magnify whatever they are seeded with.
There are a number of possible explanations for this. One is induction, a kind of social domino effect where one person’s weight gain causes another also to gain weight. The mechanisms whereby induction occurs include shifting social norms – changing ideas of what is an acceptable body size and copying the behaviour of others. Other possibilities are homophily – the tendency for people to form friendships with others similar to themselves – and joint exposure to contextual or environmental factors that influence weight gain. All of these factors are normally present to some extent in any social phenomenon.
The structure of networks
The tendency to form networks with particular structures might be genetically encoded. To understand this, we need to dissect network structure. There are different positions within networks – some nodes (people) have more connections to other nodes and some have fewer. This is known as the degree of a node. The centrality of a node’s position within a network matters, as does its transitivity – for example, whether a person’s friends all know each other or not. Genes influence the number of friends a person has and the transitivity of their friends.
Maps of the social networks of the Hadza, a Tanzanian hunter-gatherer ethnic group who live like humans in the Pleistocene era did, show that the structure of human social networks has not changed in 10,000 years.
The structure of a network is important to how the network affects its members. This is because the pattern of our connections affects the properties of our social groups. Our experience of the world depends on the structure of the ties around us.
Using networks to improve public health
We can intervene in social networks to improve public health in two ways. The first is by manipulating connections to change the structure, and the second is by manipulating contagion to change the flow of network effects.
The effect of manipulating connections was demonstrated in an experiment to influence how cooperative people are. The experiment put generous and ungenerous people into a fixed network and asked them to make choices over a time series whether to be generous to their neighbours. Generous people were forced to continue interacting with ungenerous people who had taken advantage of them in a previous interaction and, over time, cooperation in the network vanished. In contrast, when the experiment changed to allow fluid connections so that people could stop interacting with the ungenerous participants, generosity persisted.
A second experiment, set in a village in Honduras, showed the potential of manipulating contagion. The experiment assessed whether giving chlorine for water purification and multivitamins for micronutrient deficiency to a person with high centrality in the social network compared to a randomly selected person would improve the spread of the intervention. The results suggested that we should be smart about who we target – central people were more structurally influential and generated ten times the spread that randomly selected people did.
In a resource-poor healthcare environment, understanding how to target the key people in a network is crucial to maximise the impact of interventions. Careful selection enhances the response to behavioural interventions among both the treated and the untreated. It also enhances the response to treatment among the people treated by leveraging peer reinforcement.
1. Christakis N, Fowler JH. The spread of obesity in a large social network over 32 years. N Engl J Med. 2007; 357: 370-9.
2. Apicella CL, Marlowe FW, Fowler JH, Christakis NA. Social networks and cooperation in hunter-gatherers. Nature. 2012 Jan 25; 481(7382): 497-501. doi: 10.1038/nature10736.
3. Jordan JJ, Rand DG, Arbesman S, Fowler JH, Christakis N. Contagion of cooperation in static and fluid social networks. Plos One. 2013 Jun; 8(6): e66199. doi: http://dx.doi.org/10.1371/journal.pone.0066199
4. Kim DA, Hwong AR, Stafford D, Hughes DA, O’Malley, AJ, Fowler JH, Christakis NA. Social network targeting to maximise population behaviour change: a cluster randomised controlled trial. Lancet. 2015 Jul 11; 386(9989): 145-53.
This article is adapted from Professor Christakis’ keynote address at the APAC Forum 2016. Click here to see the exciting speakers lined up for the APAC Forum 2017.