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Spannend ist die Analyse der tatsächlichen Nutzung der sozialen Netzwerke, deren Ergebnisse allerdings nicht unbedingt plausibel sind.

I began to see in a very real way that the illness of the person dying was affecting the health status of other individuals in the family. And I began to see this as a kind of non-biological transmission of disease — as if illness or death or health care use in one person could cause illness or death or health care use in other people connected to him. It wasn't an epidemic transmission of a germ; something else was happening. This is a very basic observation about what I now call “interpersonal health effects,”

Nowadays, most people have these very distinct visual images of networks because in the last ten years they have become almost a part of pop culture.  But social networks were studied in this kind of way beginning in the 1950s

Georg Simmel

people like Mark Granovetter, Stan Wasserman, Ron Burt, and others

But we are of course connected to each other through vastly larger, more complex, more beautiful networks of people. Networks of thousands of individuals, in fact. These networks are in a way living, breathing entities that reproduce, and that have a kind of memory. Things flow through them and they have a purpose and can achieve different things from what their constituent individuals can.

We can start with the tiny case of a man and a woman — a pair of individuals — one of whom is sick and the other of whom cares (partly from altruistic reasons) for that person. Stepping back to see them not as individuals, but focusing on the tie that connects them as the object of inquiry, we see that they are embedded in larger sets of such networks, which forces us to engage with a set of fundamental social scientific and philosophical problems — in fact moral problems — that people have been concerned with for millennia.

These methods incidentally were built on some efforts by very well-known Hungarian mathematicians who studied a branch of mathematics known as topology, which itself has an interesting and old history stretching back to Euler. Beginning in the 1990s, there was a kind of resurrection of network science, initially caused by a group of physicists and mathematicians who were actually tackling problems in other domains. For instance, people interested in networks of genes, or cellular networks, or networks of neurons, like my colleague Laszlo Barabasi.

we are at a moment where — because of modern telecommunications technologies and other innovations — people are leaving digital traces of where they are, who they are interacting with, and what they are saying or even thinking. All of these types of data can be captured by the deployment of what I call “massive passive” technologies and used to engage social science questions in a way that our predecessors could only dream of.

Since the late 1990s and into the 2000s science more generally has been engaged in what I call the “assembly project” of modern science.

How do we reassemble all of the genes and understand how they interact with each other in space and across time?

And similarly, in social science, there is an increasing interest in the same kind of phenomenon.

We have, for example, consciousness, which cannot be understood by studying neurons. Consciousness is an emergent property of neuronal tissue. And we can imagine similarly certain kinds of emergent properties of social networks that do not inhere in the individuals — properties that arise because of the ties between individuals and because of the complexity of those ties. 

And as we have been thinking about it, we have come up with some initial simple ideas, and some initial intriguing and very novel empirical observations. The simple ideas are the following: it is critical when you think of networks to think about their dynamics. A lot of times, people fail to understand networks because they focus on the statics. They think about topology; they think about the architecture of the network.

But here is something else: Once you have recognized that there is a topology, the next thing you must understand is that there can be a contagion as well —  a kind of process of flow through the network.

Things move through it

Understanding how things flow through the network is a different challenge from understanding how networks form or evolve. It is the difference between the formation and the operation of the network, or the difference between its structure and its function. Or, if you see the network as a kind of super-organism, it is the difference between the anatomy and the physiology of the super-organism, of the network. You need to understand both.

we have started with several projects that seek to understand the processes of contagion

So we have been investigating both what causes networks to form and how networks operate.

It also includes the basic idea that there is something contagious that is spreading from person to person.

We wanted to study whether this was the case. Could obesity flow through networks?  Could one person's body type actually influence the body type of others around him, and around them, and around them, in a cascade effect? 

How did we do that?  We needed to come up with a source of data that contained information about people's position in a network, the architecture of their ties — who they knew and who those people knew and who those people knew and so forth. We also needed a source of data on people's weight and other information about them. And we needed it for a long period of time with repeated observations on these people.

What we found when we did this study is that weight gain in your friends makes you gain weight and weight gain among people beyond what we call your “social horizon” ripples through the network and affects you.

To us, it is a very, very fundamental observation that things happening in a social space beyond your vision — events that occur or choices that are made by people you don't know — can cascade in a conscious or subconscious way through a network and affect you. This is a very profound and fundamental observation about the operation of social life, which we initially examined while looking at obesity.

Moreover, people beyond those to whom you were directly tied also influenced your weight, people up to three degrees removed from you in the network. And, incidentally, we found that weight loss obeys the same properties and spreads similarly through the network.

In the case of obesity, we formulated a variety of ideas

One possible mechanism is very simple, which is biological contagion.

We are interested not in biological contagion, but in social contagion. One possible mechanism is that I observe you and you begin to display certain behaviors that I then copy.

A completely different mechanism would be for there to be not a spread of behaviors, but a spread of norms. I look at the people around me and they are gaining weight. This changes my idea, consciously or subconsciously, about what is an acceptable body size.

In our empirical work so far, we have found substantial evidence for the latter mechanism, the spread of norms, more than the spread of behaviors.

Geographic distance did not mater to the obesity effect, the interpersonal effect.

This finding, coupled with the finding regarding the lack of decay with geographic distance, suggests to us that it is a norm rather than a behavior that is spreading.  Why?  Because for a behavior to spread, typically, you and I would have to be together.

But a norm can fly through the ether.  I might see you once a year and see that you have gained a tremendous amount of weight, which resets my idea about what an acceptable body size is.

But if I see him and he has gained a lot of weight, it can change my idea about what an acceptable body size is and, in that way, the spread of the norm can cause the spread of obesity.

Happiness spreads in networks. If your friend's friend becomes happy, it ripples through the network and can make you happy. We see clusters of happy and unhappy individuals in the social network like blinking lights in this complex fabric that is made up of people where some people are happy and some people are unhappy and there is a kind of gray zone between them.

We have found that depression can spread, and drinking behaviors can spread, and the kinds of foods people choose to eat can spread (a taste for tastes can spread, as one of my graduate students is studying).

This is the difference between ideology and norms. People see these images of super models, but they might be less influenced by them than by the actions and appearance of the people immediately around them.

The real explanations for the obesity epidemic are exclusively socio-environmental — things having to do with the increasing consumption of calories in our society: food is becoming cheaper, the composition of food is changing, there is increasing marketing of foodstuffs and the like.  Also, clearly, there has been a change of rate at which people burn calories due to an increase in sedentary lifestyles, the design of our suburbs, and a whole host of such explanations.

We are not claiming that such explanations are not relevant. No doubt they are all part of the obesity epidemic. We are just saying that networks have this fascinating property whereby they magnify whatever they are seeded with.

We also mention in our paper in the New England Journal the possible relevance of so-called “mirror neurons,” which is another mechanism which I didn't touch on earlier. One possibility besides biological contagion is that by watching you exhibit certain kinds of behaviors like eating or running, I start to copy those behaviors mentally in a mirror-neuron kind of way. And this facilitates my exhibiting the same behavior.

That is, your desires and ideas can influence the structure of your network. For example, if you have ideas that foster a certain kind of ties, those ties in turn foster and support certain kinds of ideas.

When it comes to the internet, we are no longer merely talking about networks of computers or of networks of people who are in communication with each other, but we are talking about truly social networks, such as Facebook and MySpace and Friendster and LinkedIn. 

We have trawled through this large social network and grabbed information about people in the network, and their social ties, as is available on Facebook — for example, information having to do with their tastes, with the people with whom they appear in photographs, and so on.   For example, a person might have an average of 100 or 200 friends on Facebook, but they might only appear in photographs with 10 of them. We would argue that appearing in a photograph constitutes a different kind of social tie than a mere nomination of friendship.  By exploiting these kinds of data and a variety of computer science technologies, we have been able to build a network that changes across time and to trace the flow of tastes through the network (for instance, how as I start listening to a particular kind of music, you start listening to a particular kind of music). We have been able to study homophilic properties — the idea that birds of a feather flock together.  How and why do people form unions?  Do they depend upon particular attributes, tastes, and the like?  We have been able to study how these types of things — both the topology of the network and the things that flow through the network — change over time. 

But then we realized that, in addition to its conceptual importance, we could treat privacy as a taste. And we saw that the taste for privacy flowed through the network so that if I adopt privacy settings on Facebook, the people to whom I am connected will be more likely to adopt privacy settings.

So here we observe yet another phenomenon. We have talked about the flow of obesity through a network, we have talked about the flow of happiness through a network, we have talked about the flow of smoking cessation through a network, we have talked about the flow of fashions through a network. Now we are talking about the flow of tastes in privacy through the network.