skewed distributions of links that one
sees in real net works, with certain nodes
acting as highly connected “hubs.” 3
Another principle, also a key issue
in sociology, is the notion of “triadic
closure:” links are much more likely
to form between two people when they
have a friend in common. 34 Recent
work using email logs has provided
some of the first concrete measurements of the effect of triadic closure in
a social-communication network. 24
Further principles have begun to
emerge from recent studies of social and
information networks over time, including “densification effects,” in which the
number of links per node increases as
the network grows, and “shrinking diameters,” in which the number of steps
in the shortest paths between nodes can
actually decrease even as the total number of nodes is increasing. 27
It is also intriguing to ask whether
machine-learning techniques can be
effective at predicting the outcomes
of social processes from observations
of their early stages. Problems here include the prediction of new links, the
participation of people in new activities, the effectiveness of groups at
collective problem-solving, and the
growth of communities over time. 4,
16, 17, 18, 28, 37 Recent work by Salganik,
Dodds, and Watts raises the interesting possibility that the outcomes of
certain types of social-feedback effects
may in fact be inherently unpredictable. 36 Through an online experiment
in which participants were assigned
to multiple, independently evolving
versions of a music-download site—
essentially, a set of artificially constructed “parallel universes” in which
copies of the site could develop independently—Salganik et al. found that
when feedback was provided to users
about the popularity of the items being
downloaded, early fluctuations in the
popularities of different items could
get locked in to produce very different
long-term trajectories of popularity.
Developing an expressive computational model for this phenomenon is
an interesting open question.
Ultimately, across all these domains, the availability of such rich
and plentiful data on human interaction has closed an important feedback
loop, allowing us to develop and evaluate models of social phenomena at
large scales and to use these models
in the design of new computing applications. Such questions challenge
us to bridge styles of scientific inquiry—ranging from subtle small-group
studies to computation on massive
datasets—that traditionally have had
little contact with each other. And they
are compelling questions in need of
answers—because at their heart, they
are about the human and technological connections that link us all, and
the still-mysterious rhythms of the
networks we inhabit.
I thank the National Science Foundation, the MacArthur Foundation, the
Cornell Institute for the Social Sciences, Google, and Yahoo for their
support and the anonymous reviewers
of this manuscript for their comments
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Jon Kleinberg ( email@example.com) is a professor
of computer science at Cornell university, ithaca, ny.
his work focuses on issues at the interface of networks
and information, with an emphasis on the social and
information networks that underpin the Web and other
online media. he is a recipient of macarthur, packard, and
sloan foundation fellowships, and the nevanlinna prize
from the international mathematical union.