Candidate Talk: Dynamics of real networks: patterns and algorithms

With the advent of the Web, large scale social and
information networks containing detailed traces of human activity have
become available. This offers great opportunities to measure, model and
predict actions of millions of people. For example, we had an
opportunity to analyze a “planetary scale” Microsoft Instant
Messenger network that contains 240 million people, with more than 1
billion conversations per day (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 time evolving networks.
First, I will consider network cascades that are created by the
diffusion process where behavior spreads 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 four million people. Motivated by our empirical
observations we develop algorithms for finding influential bloggers and
detecting disease outbreaks. We exploit the ”submodularity” principle
to develop an efficient algorithm that achieves near optimal solutions,
while scaling to large problems and being 700 times faster than a
simple greedy algorithm. Second, in our recent work we found
interesting and counter intuitive patterns, which 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.