Title: Dynamics of Large Networks
Speaker: Jure Leskovec
Date: September 3, 2008
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
Jon Lafferty
Jon Kleinberg
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