Social networks have long been known to play a major role in determining the rate and pattern of epidemic spread of microbial diseases in human societies. Attention has focused in particular on the role of population heterogeneities and subnetworks in the spread of sexually transmitted diseases, especially HIV/AIDS; however, little work has been done on the role of network topology in the spread of other infectious diseases. More recently, physicists and computer scientists have become concerned about the spread of infectious agents (e.g., computer viruses, worms, etc.) through the Internet and the World Wide Web.
This welcome new interest in network topology has spawned a minor revolution in network modeling. It is now clear that many natural and human-made networks, from actors (the Kevin Bacon game) to the U.S. electrical power grid to the Internet, all follow a “scale-free” distribution (Barabasi, 2002). The observation that a wide variety of unplanned network topologies may follow a stereotyped pattern has led to research on how networks add nodes and grow (the “emergence” of networks); the factors involved are just beginning to be understood. Furthermore, the crucial role of occasional long-distance internodal connections in shortening global mean path lengths (the “small world” phenomenon) and accelerating epidemic spread has come to be appreciated (Watts, 1999). Recent