Dynamics of large networks - New York University
Dynamics of large networks
Jure Leskovec
Abstract
Emergence of the web and cyberspace gave rise to detailed traces
of human social activity. This offers great opportunities to analyze
and model behaviors of millions of people. For example, we examined
''planetary scale'' dynamics of a full Microsoft Instant Messenger
network that contains 240 million people, with more than 255 billion
exchanged messages per month (4.5TB of data), which makes it the
largest social network analyzed to date.
In this talk I will focus on two aspects of the dynamics of large
real-world networks: (a) dynamics of information diffusion and
cascading behavior in networks, and (b) dynamics of the structure of
time evolving networks. First, I will consider network cascades that
are created by the diffusion process where behavior cascades from node
to node like an epidemic. We study two related scenarios: information
diffusion among blogs, and a viral marketing setting of 16 million
product recommendations among 4 million people. Motivated by our
empirical observations we develop algorithms for detecting disease
outbreaks and finding influential bloggers that create large cascades.
We exploit the ''submodularity'' principle to develop an efficient
algorithm that finds near optimal solutions, while scaling to large
problems and being 700 times faster than a simple greedy solution.
Second, in our recent work we found counter intuitive patterns that
change some of the basic assumptions about fundamental structural
properties of networks varying over time. Leveraging our observations
we developed a Kronecker graph generator model that explains processes
governing network evolution. Moreover, we can fit the model to large
networks, and then use it to generate realistic graphs and give formal
statements about their properties. Estimating the model naively takes
O(N!N2) while we develop a linear time O(E) algorithm.
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