Leman Akoglu Thesis Defense
Mining and Modeling Real-world Networks: Patterns, Anomalies, and Tools
August 22, 2012, 3:00 pm EDT
Gates Hall Center 4303
CommitteeChristos Faloutsos, CMU
Andrew Moore, Google Inc., CMU
Aarti Singh, CMU
Andrew Tomkins, Google Inc.
DocumentThe document can be found here.
Large real-world graph (a.k.a network, relational) data are omnipresent, in online media, businesses, science, and the government. Analysis of these massive graphs is crucial, in order to extract descriptive and predictive knowledge with many commercial, medical, and environmental applications. In addition to its general structure, knowing what stands out, i.e. anomalous or novel, in the data is often at least, or even more important and interesting.
In this thesis, we build novel algorithms and tools for mining and modeling large-scale graphs, with a focus on:
List of references (Completed Work)
The following publications are referenced in the document (in reverse chronological order).