| Abstract: |
A basic premise behind the study of large networks is that interaction
leads to complex collective behavior. In our work we found interesting
and counterintuitive patterns for time evolving networks, which change
some of the basic assumptions that were made in the past. We then
develop network models, fit such models to real networks, and use them
to generate realistic graphs or give formal explanations about their
properties.
Another important aspect of our research is the study information
diffusion and the spread of influence in a large person-to-person
product recommendation network and its effect on purchases. We also
model the propagation of information on the blogosphere, and propose
algorithms to efficiently find influential nodes in the network.
A central topic of our thesis is also the analysis of large datasets as
certain network properties only emerge and thus become visible when
dealing with lots of data. We analyze the world's social and
communication network of Microsoft Instant Messenger with 240 million
people and 255 billion conversations. We also made interesting and
counterintuitive observations about network community structure that
suggest that only small network clusters exist, and that they merge and
vanish as they grow.
To view a draft of the thesis see:
http://www.cs.cmu.edu/~jure/pubs/thesis/jure-thesis.pdf
COMMITTEE: Christos Faloutsos, Avim Blum, John Lafferty, Jon Kleinberg
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