ample, some people have many more “links” than average—an important issue in understanding the spread of HIV.) And real networks form clusters, like cliques of close friends.
Erdös and Rényi knew full well that their dots and lines did not capture the complexities of real-world networks. As mathematicians, they didn’t care about reality—they developed their model to help understand the mathematical properties of random connections. Describing random connections was a mathematically feasible thing to do; describing all the complexities of real-world networks was not. Nobody knew how to go about doing it.
But then a paper appearing in the British journal Nature began to change all that. Looking back, the birth of network mania can be dated to June 4, 1998, when Duncan Watts and Steven Strogatz published a brief paper (taking up only two and a half Nature pages) called “Collective Dynamics of ‘Small-World’ Networks.”3
A few years later, when I met Strogatz at a complexity conference, I asked him why networks had become one of math’s hottest topics in the late 1990s. “I think our paper started it,” he said. “If you ask me when did this really start, I think it started in 1998 when our paper appeared in Nature on what we called small-world networks.”
So I quizzed Strogatz about that paper’s origins. It really was a case of culture preparing the conditions for the advance of science.
“When Watts and I started our work in 1995 or so, we were very aware of the whole Kevin Bacon thing, and we had heard of six degrees of separation, and the movie had come out of that play,” said Strogatz. “So it was in the air.”4
Of course, Kevin Bacon didn’t revolutionize science totally on his own. The Bacon game became famous just about the time that the public became aware of the Internet, thanks to the arrival of the World Wide Web.
“I think the Web got us thinking about networks,” Strogatz