Cascading Behavior in Complex Networks
Jon Kleinberg, Cornell University
In many domains, the spread of information through a network can
trigger forms of cascading behavior. We focus particularly on large
social networks, where the flow of information or influence can be
thought of as unfolding with the dynamics of an epidemic: as individuals
become aware of new ideas, technologies, fads, rumors, or gossip,
they have the potential to pass them on to their friends and colleagues,
causing the resulting behavior to cascade through the network.
We consider a collection of models for such phenomena proposed in the
mathematical social sciences, as well as recent algorithmic work on
the problem by computer scientists. Building on this, we discuss the
implications of cascading behavior in a number of settings, relating
the basic models to recent empirical studies of large-scale on-line
network data.
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