Complexity_ A Guided Tour - Melanie Mitchell [110]
However, over the last decade or so, a growing group of applied mathematicians and physicists have become interested in developing a set of unifying principles governing networks of any sort in nature, society, and technology. The seeds of this upsurge of interest in general networks were planted by the publication of two important papers in the late 1990s: “Collective Dynamics of ‘Small World Networks’ ” by Duncan Watts and Steven Strogatz, and “Emergence of Scaling in Random Networks” by Albert-László Barabási and Réka Albert. These papers were published in the world’s two top scientific journals, Nature and Science, respectively, and almost immediately got a lot of people really excited about this “new” field. Discoveries about networks started coming fast and furiously.
Duncan Watts (photograph courtesy of Duncan Watts).
The time and place was right for people to jump on this network-science rushing locomotive. A study of common properties of networks across disciplines is only feasible with computers fast enough to study networks empirically—both in simulation and with massive amounts of real-world data. By the 1990s, such work was possible. Moreover, the rising popularity of using the Internet for social, business, and scientific networking meant that large amounts of data were rapidly becoming available.
In addition, there was a large coterie of very smart physicists who had lost interest in the increasingly abstract nature of modern physics and were looking for something else to do. Networks, with their combination of pristine mathematical properties, complex dynamics, and real-world relevance, were the perfect vehicle. As Duncan Watts (who is an applied mathematician and sociologist) phrased it, “No one descends with such fury and in so great a number as a pack of hungry physicists, adrenalized by the scent of a new problem.” All these smart people were trained with just the right mathematical techniques, as well as the ability to simplify complex problems without losing their essential features. Several of these physicists-turned-network-scientists have become major players in this field.
Steven Strogatz (photograph courtesy of Steven Strogatz).
Albert-László Barabási (photograph courtesy of Albert-László Barabási).
Perhaps most important, there was, among many scientists, a progressive realization that new ideas, new approaches—really, a new way of thinking—were direly needed to help make sense of the highly complex, intricately connected systems that increasingly affect human life and well-being. Albert-László Barabási, among others, has labeled the resulting new approaches “network thinking,” and proclaimed that “network thinking is poised to invade all domains of human activity and most fields of human inquiry.”
What Is Network Thinking?
Network thinking means focusing on relationships between entities rather than the entities themselves. For example, as I described in chapter 7, the fact that humans and mustard plants each have only about 25,000 genes does not seem to jibe with the biological complexity of humans compared with these plants. In fact, in the last few decades, some biologists have proposed that the complexity of an organism largely arises from complexity in the interactions among its genes. I say much more about these interactions in chapter 18, but for now it suffices to say that recent results in network thinking are having significant impacts on biology.
Network thinking has recently helped to illuminate additional, seemingly unrelated,