Learning and Mining on Complex Networks
Complex networks are ubiquitous in many domains. Examples include technological, informational, social, and biological networks. In this talk, I will present algorithms for both relational classification and clustering in such networked data. I will pay special attention to issues surrounding scalability, sparsity of labels, various levels of relational dependency, and performance consistency across assorted domains.
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- Fairleigh Dickinson University
- Teaneck, New Jersey
- United States 07666
- Building: Auditorium M105, Muscarelle Center
- Click here for Map
- Contact Event Host
- Hong Zhao (201)-692-2350, zhao@fdu.edu; Howard Leach h.leach@ieee.org
- Co-sponsored by School of Computer Sciences and Engineering, FDU
- Starts 16 March 2011 10:00 PM UTC
- Ends 21 April 2011 10:00 PM UTC
- No Admission Charge
Agenda
Dr. Tina Eliassi-Rad is an Assistant Professor of Computer Science at Rutgers University. Until September 2010, Tina was a Member of Technical Staff and Principal Investigator at Lawrence Livermore National Laboratory. Dr. Eliassi-Rad earned her Ph.D. in Computer Sciences (with a minor in Mathematical Statistics) at the University of Wisconsin-Madison in 2001. Broadly speaking, Dr. Eliassi-Rad's research interests include data mining, machine learning, and artificial intelligence. Her work has been applied to the World-Wide Web, text corpora, large-scale scientific simulation data, complex networks, and cyber situational awareness. Dr. Eliassi is an action editor for the Data Mining and Knowledge Discovery Journal. In 2010, she received an Outstanding Mentor Award from the US DOE Office of Science and a Directorate Gold Award from Lawrence Livermore National Laboratory for work on cyber situational awareness. For more info, visit http://www.eliassi.org