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

From Edge, this is an interesting talk by Albert-lászló Barabási on thinking in terms of networks. Some of the questions he has been asking and seeks to answer here are:

• [W]hat does it mean to be part of the network? 
• [W]hat does it mean to think in terms of the network? 
• What does it mean to take advantage of this connectedness and to understand that? 
• [H]ow do you describe mathematically the connectedness? 
• How do you get data to describe that? 
• What does this really mean for us?

Some of the implications of this research might be alarming for some. I have no problem with collected data and trying to understand how, when, and why people interact, but I have concerns about what might happen to that data - who is going to use it, and how is it going to be used?

You can listen to the audio link below, watch the embedded video, or read the transcript (the first paragraphs of which are included, but you'll need to follow the title link to read the whole thing).

Audio: Thinking In Network Terms

THINKING IN NETWORK TERMS  

A Conversation with Albert-lászló Barabási [9.24.12] 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?  

ALBERT-LÁSZLÓ 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.

Albert-László Barabási Edge Bio Page

THINKING IN NETWORK TERMS

[ALBERT-LÁSZLÓ BARABÁSI: 9/24/2012]

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?