The physicist and the political scientist discuss contagion and the Obama campaign, debate the natural selection of robustness and ask whether society is turning inward.

Using the lingua franca of math, Albert-László Barabási describes networks in the World Wide Web, the internet, the human body, and society at large. James Fowler seeks to identify the social and biological links that define us as humans. But whereas Barabási sees similarity across systems, Fowler believes that the underlying principle in social networks may be inherent variation. Over a recent lunch in Boston, they discussed contagion and the Obama campaign, debated the natural selection of robustness, and asked: Is society turning inward?

ALBERT-LÁSZLÓ BARABÁSI: It is becoming a truism that we’re living in the era of networks. Just about anywhere we turn, we encounter one. We have the World Wide Web and the internet; we have social networks, genetic networks, and biochemical networks. These things — web pages, genes, chemicals in our cells — are nothing new. What is new is that everybody’s waking up to the fact that there is a network behind all of these systems, and we need to think about networks as a common feature of all complex systems. But I don’t know if that’s the way you see it.

JAMES FOWLER: Well, as a social scientist, I’m always asking, “Why do people do stuff?” So for me, what is most amazing about networks is that they completely transform the way we think about data. For a really long time, we’ve thought about individuals as though they were islands — a Robinson Crusoe model of social science. Being able to integrate information — not just about people, but about their relationships — is something that’s completely new.

The rise of online social networks in the past few years has been very important in this respect. Now we can ask, “What’s happening in that whole complex set of relationships that we could never learn by looking at just each individual?”

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ALB: Social networks have also given us a new cache of hard data so we’re no longer talking so abstractly about networks. One of the fundamental surprises, which certainly excites the physics community, is that we keep finding similar organizing principles across widely different systems. That is, if for a moment you forget that one node is a metabolite, the other is a gene, and the third is a person, the networks behind metabolism, genetics, and social systems are very much alike. And this has allowed people — social scientists like you, physicists like me, as well as biologists and economists — to talk together on equal terms.

JF: It’s really breaking down barriers. And I completely agree that it’s data driven, that we have this new information, especially about interactions between people and interactions at the cellular level, that has driven an interest in these methods. But the methods are now driving interest in the data. There’s a lot more interest, for example, in how to define the nodes and the relationships in, say, a set of college students in a dorm. So if we asked them, “Who are your friends?” we can now follow who they call by tracking their cellphone usage, or by tagging their phones with GPS to see who they physically spend time with.

Getting that kind of massive, passive data has now become a goal, because we have these network methods that you and your colleagues have developed.

ALB: Which means that there is this huge train of digital fingerprint data coming toward social science, capturing in details just about anything humans do. Are you prepared for that?

JF: Well, the great thing about these massive, passive data sets is that we’re going to have really deep information about a very, very large number of people. So we won’t be forced anymore to make trade-offs between depth and breadth. But then the question becomes: What kind of preparation are we going to give our students? We’ve had a revolution in game theory in the past 30 years, so that a good number of political scientists all across the country work only on mathematical, closedform models. We’ve also had a revolution in the application of statistics.

But both of these revolutions have been built on this atomistic view of human beings. Statisticians make the assumption that all the observations are independent in order to be able to calculate statistical significance. Game theorists make it because, as you know, getting anything to work out in a closed-form model is nearly impossible if you assume that people are taking into account the preferences of other people.

We need not only to ramp up the amount of methodological training that people in social sciences have, but also to shift their perception into realizing that the relationships between people are important.

ALB: But that process and the accompanying change of perspective can create a huge amount of stress. It certainly did in genetics, where careers are traditionally made by discovering one gene and focusing deeply, in a reductionist way, on what that particular gene does.

Suddenly, a new generation of physicists, biologists, mathematicians, bioinformaticians, systems biologists — whatever you want to call them — comes along and says, “You know, I don’t have a favorite gene. I want to look at all of them simultaneously.” That is a fundamental change in the view of what really matters in biology, one that not everybody is ready for. It’s creating stress. Do you see this happening in sociology?

JF: Well, in political science we basically try to answer two fundamental questions: “How do we organize ourselves to achieve something that we couldn’t as individuals?” and “Once we achieve that, how do we decide who gets what?” We’ve made progress in terms of figuring out individual decision making, but I don’t know how you get further on these questions without networks.

So for me, networks present a tremendous opportunity. It’s like when Leeuwenhoek first looked at the structure of the cell and for the very first time was able to connect things inside the cell to the way the cell functioned.

ALB: Let’s stay with political science for a second. The question that everybody asks about networks is: “So what? Do we ever see their consequences?” Recently people have pointed to the Obama campaign and said, “This is where network science leads you.”

JF: Yes. A lot of this stuff started with the Dean campaign, realizing the importance of using the internet to mobilize people and accessing social networks. So, rather than knocking on someone’s door or cold-calling them, you help them have coffee with one another, where they’ll discuss the candidates. So you’re promoting social interaction. What Obama did is take this to a larger scale.

The second thing they did, which was counterintuitive to some economists, is they allowed people to donate any amount of money they wanted to — even donations as small as a dollar. The fixed cost of them doing that was actually pretty high.

But they realized that once you give a little bit, you’re probably more motivated to give a lot later on. They also understood that once you give, the behavior spreads in your social networks. You say to your friends, “I gave money to the Obama campaign.” They hear that, and then they’re more inclined to give.

So his fundraising was just off the charts. I’m sure that as we read the tea leaves, we’re going to find that social networks were a big part of the story. I also think, going forward, that every campaign is going to be run this way, because they’re going to see how successful this one was.

Campaigns in politics are sort of like evolution, right? Because only the survivor wins, and people will try to copy the better strategy.

ALB: Which actually leads to an interesting question: the spread of networks. You just mentioned how the urge to give money spreads through social networks. And you and Nicholas Christakis also did that wonderful study on the impact of social networks on health. My group studies how an error in the network within the cells spreads along the genetic links, leading to multiple diseases. There is really a paradigm change here. We always hear about diseased genes, but what we are learning through networks is that when a disease emerges, it’s typically because some part of the network breaks down in your cell. There is no single cancer gene. There are actually closer to 300 genes associated with cancer — 

JF:  — that we know of so far.

ALB: That’s right. And they break down in different combinations, though they all lead to the same type of cancer. It’s very puzzling to everybody. So I often give this analogy: If you go out in the morning to start your car and the lights don’t go on, there could be lots of reasons. It is perhaps because the battery is dead or maybe a cable is broken, or the switch is not working, or your lightbulb is broken. Or maybe a fuse is gone.

Once you take it to the shop, the mechanic can simply pull out the wiring diagram and check a few points; within five minutes, he diagnoses the problem and then can replace the right component. We don’t yet have the wiring diagrams of cellular networks and we are missing the spare parts too. An important goal in biology and medicine is to get those diagrams.

So this is one paradigm change. On top of this you and Nicholas have been working on how the social network would have just as much impact on disease as the cellular network.

JF: Yes. I was interested in how political behavior could spread through social networks. So some of the very first work I did was on the question: “If I vote, how does it affect my friends and family?” Nicholas had a very similar history, but he was interested in health. He had all this work on spouses, how if one spouse dies it can cause the other spouse to die prematurely, for example. And so we wondered, “Why would it stop there?” If something is happening to me, and it has an effect on you, well, you’re going to have friends, and they’ll have friends, too. So even though there’s only a small chance that I’ll have a significant impact on you, that small chance gets multiplied. Pretty soon, in the network, you’re talking about dozens, hundreds, thousands of people who are going to be indirectly influenced.

As it turns out, we found this for obesity. Weight gain and weight loss can spread from person to person to person through the network, up to three degrees of separation. We also found this for smoking and, more recently, for happiness.

We have these connections, so we can see things we’ve never seen before — like what happens to a person who’s three degrees removed. Again, this is like Leeuwenhoek looking inside the cell for the first time.

Facebook in a crowd

JF: I’ve noticed something interesting on Facebook. I’ll have a cluster of friends who are not on Facebook and when one becomes my friend, all of a sudden — in a matter of days — they will all become friends with me, and all become friends with one another until the community is linked.

ALB: That’s right. And then it freezes until some other friends come along and connect another community to you. The phenomenon is not unique to Facebook; the web also evolves through bursts.

JF: Exactly. And in Facebook it’s not so easy for links to go away.

ALB: It’s a wonderful example of how our world has changed thanks to technology. We all have friends whom we’ve accumulated over 20, 30, 40 years, depending on our age. I moved from Transylvania to Hungary and from Hungary to the United States. In the past, most of my previous links were lost. Now they’re all on Facebook and the Hungarian equivalent, called iWiw. Suddenly, these social networking sites become a depository of our personal history. Some elementary school friends recently reconnected with me. People about whom I have to think hard, “Who are they?” Then, I remember, “Oh yes, he was in my fourth grade class.” Now he’s a friend. Because of technology we have stopped hemorrhaging links. And I think this is fundamentally changing how we behave on a daily basis.

JF: I agree. But if we move from five friends in real life to 500 on Facebook, it’s not the case that we are having a close, deep relationship with each of those 500 friends.

ALB: Sure.

JF: In fact, one of the intriguing things I’ve noticed about these online networks is that they have a property that’s different from realworld social networks. As you know, in the real world, popular people tend to be friends with popular people. But in these technological networks, as in metabolic networks, it’s just the opposite. The nodes with many, many links will tend to be linked to nodes with few links.

ALB: Right.

JF: It makes me wonder if the dynamics of online social networks are going to be reflective of realworld social networks. Because to a large extent, in your work and some of the work that I’ve done, we’re relying on the idea that what we see online is telling us something about the real world. But there’s a pretty fundamental difference.

ALB: Which brings up a good point: What do we mean when we say that all the “real-world” networks —  the technological, social, and metabolic ones — are similar to each other? They share a few fundamental organizing principles. One that has gotten lots of attention in the scientific community is the existence of the hubs. All of these extremely disparate networks, from the cell that has developed over 4 billion years to the World Wide Web with a 20-year history, have naturally developed these hubs. Somehow, networks always converge to the same underlying scale-free structure.

JF: This really takes us back to Darwin. Which for me, in the social sciences, is a little controversial. But I believe we’re going to find that natural selection is what causes hubs to emerge in all these different networks. You have natural selection operating in the cell. You have it operating on the evolution of the brain. And recently, Nicholas Christakis, Christopher Dawes, and I have found evidence that there’s a genetic basis for human social networks — that the number of people who name you as a friend is actually heritable, and about half of the variation in the number of friends can be explained due to variation in the genes.

ALB: So you mean my genes affect how many people would name me as a friend?

JF: Yes.

ALB: Can I get that gene?

JF: Some of this makes sense, right? Physically attractive people, people who communicate well, people who have assets, are probably going to be more attractive. But we were startled at how strong the genetic effect was. To me, what this really says is that human social networks have been operating under natural selection for a very long time — since we were walking around on the plains of the Serengeti in the Pleistocene. These forces are still with us today. So I really appreciate the effort to explain the variation between the hubs and the isolates in these physics models using a single underlying principle. But what this suggests to me is that it’s not necessarily inherent homogeneity or similarity that brings about some of this variation; it’s actually inherent variation.

There are things that make each of us, as human beings, unique — that give each of us a unique place in the social network. The fact that we’re finding this sort of genetic relationship makes me wonder if there’s actually a genetic purpose. That is, natural selection might have acted on us to make sure that we have a variety of people who are hubs and who aren’t hubs, that we have people within these dense networks, as well as people who act as bridges between groups.

ALB: Wow. I have never heard that one. It’s interesting, because the question of the role of natural selection came up very sharply when we first started to look at cellular networks, when we really didn’t know what to expect. When the data came out, in each case we saw the same scale-free structure, which forced us to say, “Why so?” And now the understanding is that it is because of growth — the fact that each network emerges through the gradual addition of new nodes. The growth process imposes such strong constraints on the network structure that all natural selection does is choose among the many possible scale-free networks. In the case of biological systems, we understand why the cell is scale free. What biologists have shown is that if the main mechanism by which you add new genes to the cell is by gene duplication — that is, you copy and recopy existing genes — then the only network that can emerge from that process is a scale-free network.

JF: So, where does it come from? I mean, if they’re all scale free, then that suggests that natural selection isn’t the cause.

ALB: Right. And actually, one of the important properties of scale-free networks is their robustness. That is, if you start randomly removing nodes from a scale-free network, the network will not collapse. Which initially led us, and many others, to think, “Well, then the reason why the cell is scale free, the reason why the hubs are there, is because of this robustness. It’s good for the cell, therefore natural selection has led the network to be scale free.” Yet nobody has managed to produce a scale-free network that is built on the robustness principle. If you try to optimize a network to be very robust to random node removal, to breakdowns, you’ll never find a scale-free one.

This suggests to us that the scale-free state of the cell, the existence of the hubs, is not because the cell has optimized itself to be resilient against mutations and other types of errors. It’s really coming from the way the cell — just like the internet — is created from the growth process, one node at a time. Since hubs happen to be a desirable property, there is no reason for natural selection to delete them.

The Responsible Network

JF: People have always been aware of their friends, but for the first time, we’re becoming aware of friends of friends of friends. It’s going to be interesting to see if this changes behavior. I know that as a result of our studies on obesity, and especially on happiness, my own behavior has changed.

You can think about it in one of two ways. If you think that you are tied to all these people that you don’t know and have never met, yet they are going to have an influence on you, you might just feel like, “Geez, I have no free will. I might as well just give up. I am just a piece of flotsam on the sea, floating up and down with the movements of everybody on the network.”

But the other way to react is to take responsibility for all those people because they are also influenced by you. I have noticed that it has been easier for me to lose weight now, for example. And when I am walking home from the bus stop, I make sure to put on my favorite song. Because I know now that if I enter my house in a crummy mood, I’m not just going to make my son unhappy and my wife unhappy; I’m going to make my son’s friend and my wife’s mother unhappy. There are going to be all sorts of indirect, unintended consequences of my behavior, which makes me feel that I should take more responsibility.

I really think that the feeling of being connected is, on balance, going to be a very positive thing for our society.

ALB: Interesting. I mean, I don’t know much about the psychological aspect of networks, but one of the things that I find fascinating is Milgram’s iconic Six Degrees of Separation experiment — where people were asked to pass messages to a certain person who has to be reached. When you look at the social network as a whole, you see the hubs. But when you look where people pass the messages, the hubs are missing.

Somehow, when people are asked to participate in a game, they’re avoiding the hubs, even though they know that the hubs would be the most efficient way to spread the message. Basically, it’s like saying, I would never pass it to my president, because I would not bother him with such a silly thing. So this is one example where we know that we have a completely different attitude toward the hubs than the nonhubs. It comes back to the psychology of how we’re going to handle these links, and the way we handle them is that we differentiate between people.

JF: Right.

ALB: But that reminds me, I want to actually test an idea out on you — 

JF:  — you’ve saved it for the end!

ALB: Yes, exactly! It’s unquestionable that the 20th century brought us rapid understandings of the cosmos and elementary particles. We developed quantum theory, built huge accelerators, went to the Moon. We explored everything, from the very small to the huge. In contrast, the 21st century the century has been called the century of networks, of complexity. And as network thinking emerges and explodes, we’re also seeing a drop in the interest towards the traditional problems in science. I don’t know whether the change is good or not, but we certainly seem to have lost the appetite to go to the small and to the big.

I mean, my son doesn’t want to be an astronaut any longer. I’ve asked him many times: “Would you like to go to the Moon?” And he says: “No. I don’t care about that.” But he cares deeply about Facebook and about the internet. He cares deeply about the web. At the same time, students who in the past would have gone to physics and math, now are enrolling in computer science and biology, or are trying to understand networks and complexity. So I think that this network explosion coincides with humanity turning inward.

I wondered if you felt the same?

JF: Yeah. Part of this turning inward is a function of the fact that people like you would be out of a job if you didn’t start thinking about social science, right? So many of the outstanding questions in physics have been resolved that a lot of your colleagues are turning to other places to use their tools. The other part, I think, is that because we’ve already maxed out on the negative end of technology — with the creation of things like global warming and nuclear weapons — there has been a realization that maybe we ought to be putting our best and brightest minds at work on this question of how we all get along.

And it couldn’t have come at a moment too soon. Because the challenges we are going to face this century are truly astounding. It’s an open question whether or not we are going to make it until the end of the century. But I think that if we are going to make it, it’s only because we’re able to understand ourselves better by using this new technology. That’s really going to be what helps us find solutions to these problems that we face in the century of networks.

ALB: Amen.


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