Interdisciplinary Research
(and some recent results) on the Small-World Problem
Duncan Watts
Traditionally, social science has been the beneficiary
of concepts and
techniques derived from the natural sciences, such as mathematics and
statistics, physics, and biology, but rarely have they returned the favor.
The field of network analysis, however, is showing some promise of becoming
an exception to the rule. While many of the early ideas of social network
analysis (density, centrality, random-biased networks, block-modeling), had
origins in mathematics and solid state physics, and a number of recent
advances in network research (modeling of partly-random networks,
non-normal degree distributions, dynamical evolution of networks, and
network models of contagion) have come from outside sociology, it is also
true that ideas and empirical evidence from sociology, particularly with
regard to social networks, have begun to penetrate other disciplines. In
this talk, I argue that social network analysis, properly construed,
provides us a rare opportunity not only to adopt ideas from the natural
sciences to address sociological problems, but to help natural scientists
apply sociological ideas to their own problems. As an example, I follow
the history of a single problem-The Small World Phenomenon-from its origins
as an exclusively sociological question to a veritable cottage industry in
statistical physics and other fields. Furthermore, while a few of the
general features of the original research are well known, I show that the
small-world phenomenon is still yielding insights which shed light on
emerging problems such as efficient search algorithms for peer-to-peer
networks