Q&A: Albert-László Barabási on the diversity of networks

Network science is a new and rapidly growing field, with applications ranging from protein interactions to the internet. However, the sheer breadth of areas in which network analysis might be useful presents a daunting challenge to potential textbook authors.

In Network Science (Cambridge University Press, 2016), Albert-László Barabási tackles that challenge. The result, writes reviewer Zoltán Toroczkai in the April issue of Physics Today, is “a hands-on and engaging textbook” that also makes good use of accompanying online resources, such as animations and other pedagogical tools.

Physics Today recently talked to Barabási about his pioneering contributions to network science, the challenges of writing a textbook in an interdisciplinary field, and his approach to developing online resources.

PT: How did you become interested in networks as a field of study?

BARABÁSI: In 1994, after starting my postdoc in the theoretical physics division at the IBM Watson Research Center, I became curious about what IBM does, and I began reading about the fundamentals of computer science. It was in this context that I encountered networks. I realized that traditional models assume that real networks are randomly wired, even though they could hardly function if that were the case. So I started working on networks, bringing a statistical physics perspective to them.

I published my first paper on networks in Physical Review Letters in 1996. It focused on a boring network, a lattice, but it got me started. I also wrote another paper , more in the spirit of what we call network science today, that I simply could not get published. It was rejected by four physics journals, and not because the referees found anything wrong with it. Instead, they kept wondering, “Why do we care?” It wasn’t until 1999 that we network science researchers could finally get our papers published.

PT: Network Science contains incredibly diverse examples of networks, including power grids, social media, guests at a party, and airport congestion. How did you approach the challenge of writing a textbook on a field that is so interdisciplinary and has such wide-ranging applications?

BARABÁSI: The challenge preceded the book writing—I encountered it while preparing a class that I had originally designed for physicists. The audience expanded, and more and more computer science, biology, and economics students started to attend. With that shift, the examples expanded. The course evolved by acquiring more interdisciplinary examples and applications while maintaining its statistical physics–inspired core.

In writing the book I faced another challenge: How do I make it accessible to both undergraduate and graduate students? In the end, I achieved that by focusing in the main text on the conceptual questions and the key results, and delegating the more involved derivations and analytical issues to sections on “Advanced Topics” that are geared toward graduate students.

PT: One of your most famous contributions to network science has been the concept of scale-free networks. What is a scale-free network, and why might that attribute be significant?

BARABÁSI: In scale-free networks the degree distribution, which captures the number of links for each node, follows a power law. This is in contrast with random networks, in which the distribution of links follows a Poisson form. Scale-free networks are significant for two reasons. The first is that the power law allows for hubs, which are nodes with an exceptional number of links. Once such hubs are present, they fundamentally change the system’s behavior—they are very effective at spreading viruses, for example—or confer robustness to the network. The second reason is universality: While we discovered scale-free networks in the context of the World Wide Web, subsequently researchers found that most biological, technological, and social networks are in fact scale-free. That includes the protein interaction networks in our cells, the infrastructure of the internet, and social networks like Facebook and Twitter.

PT: Network Science has a number of online resources associated with it, including teaching resources, animations, and a Facebook page . How did you design and develop those online components, and what do you think they add to the book?

BARABÁSI: The book was designed to be a textbook; hence it was natural that I share the resources I use teaching it, from videos to slides, to make it easy for instructors who would like to take up the subject. At the same time, there are so many fantastic videos and interactive features that illuminate the properties of networks and their applications that I felt it would be a shame not to offer them to students. The online resources were the best way to achieve that. And we plan to expand it further: Soon we will start posting the translations in several languages, from Russian to Spanish, to expand the pool of students and instructors who can benefit from the book.

PT: What are you currently reading?

BARABÁSI: Seventh Sense, by Joshua Cooper Ramo, which exposes the power of networks in politics and business.

PT: What is your next project?

BARABÁSI: I am working on a general audience book titled The Formula: The Quantitative Science of Success, which unveils the relationship between performance and success in the era of big data. There is a simple fact at its heart: Performance is what we do, but success is what our community and environment do to us, by appreciating―or ignoring―our performance. It will discuss scientific success as well: When does a breakthrough happen in the life of scientists? And what are the network and societal forces that pull some discoveries or scientists into the limelight yet ignore other, equally novel and innovative efforts? Once the book is finalized, I hope to return to the Network Science textbook and add two or three more chapters on topics that I did not have the energy to cover in the first edition, like weighted networks, spatial networks, and network biology.