Title: Interactive Knowledge Discovery within Near-Terascale Graphs
Speaker: Steve Reinhardt, Interactive Supercomputing, Inc.
Date: Monday, October 20, 2008 11:00 - 12:00 pm**
**Note Special Time
Location: DyDAn Center, CoRE Bldg, Room 431, Rutgers University, Busch Campus, Piscataway, NJ
Abstract:
Numerous problems in homeland security, biology (e.g., metabolic network analysis), and social network analysis are well represented by graphs. The meteoric rise in the amount of data available often makes these graphs extremely large by traditional standards. Analysis of these graphs needs to be interactive and exploratory, as the best methods are not known a priori. We have built a small suite of algorithms in MATLAB and Python believed to be useful for very large graphs, including simple graph operations (e.g., union), graph traversal (minimum spanning tree), and dimensionality reduction (e.g., SVD and non-negative matrix factorization). By using the Star-P parallel platform, we have scaled these algorithms to graphs of nearly a terabyte, running on >100 cores to achieve interactivity. This presentation will cover details of the Knowledge Discovery Suite, future plans, and several recent applications.
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