Thinking in Network Terms: A Conversation with Albert-lászló Barabási

BarabásiHere is a portion of the transcript of Albert-lászló Barabási‘s conversation with John Brockman, founder and editor-in-chief of To watch the video and/or read the entire transcript, please click here.

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One question that fascinated me in the last two years is, can we ever use data to control systems? Could we go as far as, not only describe and quantify and mathematically formulate and perhaps predict the behavior of a system, but could you use this knowledge to be able to control a complex system, to control a social system, to control an economic system?

We always lived in a connected world, except we were not so much aware of it. We were aware of it down the line, that we’re not independent from our environment, that we’re not independent of the people around us. We are not independent of the many economic and other forces. But for decades we never perceived connectedness as being quantifiable, as being something that we can describe, that we can measure, that we have ways of quantifying the process. That has changed drastically in the last decade, at many, many different levels.

It has changed partly because we started to be aware of it partly because there were a lot of technological advances that forced us to think about connectedness. We had Worldwide Web, which was all about the links connecting information. We had the Internet, which was all about connecting devices. We had wireless technologies coming our way. Eventually, we had Google, we had Facebook. Slowly, the term ‘network connectedness’ really became part of our life so much so that now the word ‘networks’ is used much more often than evolution or quantum mechanics. It’s really run over it, and now that’s the buzzword.

The question is, what does it mean to be part of the network, or what does it mean to think in terms of the network? What does it mean to take advantage of this connectedness and to understand that? In the last decade, what I kept thinking about is how do you describe mathematically the connectedness? How do you get data to describe that? What does this really mean for us?

This had several stages, obviously. The first stage for us was to think networks, only networks down the line. That was about a decade ago, we witnessed the birth of network science. I could say a couple of geniuses came along and did it, but really it was the data that made it possible. Suddenly we started to discover that lots of data that’s out there, that we’re collecting thanks to the Internet and other technological advances, allowed us to look at connectedness and to measure it and to map it out.

Once you had data, you could build theories. Once you had theories, you have predictive power, you could test that and then the whole thing fitted itself. It suddenly very actively emerged as a field that we now call network science. Going beyond networks, going beyond connectedness, we realized we started to know not only whom you connect to and whom you see and where are your links (the economical, personal, social or whatever they are) but we started to see also the timing of your activities. What do you do with those links? When do you interact?

That was the second way; we called it “human dynamics.” It describes what do we do in real time, because if you think about it, social sciences have been trying for a very, very long time to try to describe human behavior. They did a really good job delivering a set of tools for how you measure a person’s activity, but much of that was really based on observation, based on small samples and based on interviews and questionnaires. What has happened in the last decade or so is that thanks to the many activities we have, and thanks to the many digital devices that we carry around, much of our activity became completely recorded. We got to the point that there’s so much data recording happening around us, that pretty much somebody who lives in a big city in Western Europe or in the United States, much of their life, almost in minute resolution, can be reconstructed from the many data streams that we leave around us.

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To watch the video and/or read the entire transcript, please click here.

Albert-lászló Barabási‘s work on complex networks lead to the discovery of scale-free networks in 1999, and he proposed the Barabasi-Albert model to explain their widespread emergence in natural, technological and social systems, from the cellular telephone to the WWW or online communities.

Currently, Barabási is a Distinguished University Professor at Northeastern University, where he directs the Center for Complex Network Research, and holds appointments in the Departments of Physics, Computer Science and Biology, as well as in the Department of Medicine, Harvard Medical School and Brigham and Women Hospital, and is a member of the Center for Cancer Systems Biology at Dana Farber Cancer Institute.

A Hungarian born native of Transylvania, Romania, he received his Masters in Theoretical Physics at the Eötvös University in Budapest, Hungary and was awarded a Ph.D. three years later at Boston University. After a year at the IBM T.J. Watson Research Center, he joined Notre Dame as an Assistant Professor, and in 2001 was promoted to the Professor and the Emil T. Hofman Chair.

Barabási is a Fellow of the American Physical Society. In 2005 he was awarded the FEBS Anniversary Prize for Systems Biology and in 2006 the John von Neumann Medal by the John von Neumann Computer Society from Hungary, for outstanding achievements in computer-related science and technology. In 2004 he was elected into the Hungarian Academy of Sciences and in 2007 into the Academia Europaea.

Barabási is the author of Linked: The New Science of Networks, currently available in eleven languages. He is the co-author of Fractal Concepts in Surface Growth (Cambridge, 1995), and the co-editor of The Structure and Dynamics of Networks (Princeton, 2005). His work on complex networks have been widely featured in the media, including the cover of Nature, Science News and many other journals, and written about in Science, Science News, New York Times, USA Today, Washington Post, American Scientist, Discover, Business Week, Die Zeit, El Pais, Le Monde, London’s Daily Telegraph, National Geographic, The Chronicle of Higher Education, New Scientist, and La Republica, among others. He has been interviewed by BBC Radio, National Public Radio, CBS and ABC News, CNN, NBC, and many other media outlets.

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