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GTC ON-DEMAND

Accelerated Data Science
Presentation
Media
Spectral Clustering of Large Networks
Abstract:
We'll explore techniques for expressing graph clustering as an eigenvalue problem. Attendees will learn how to express different metrics, including minimum balanced cut, modularity, and Jaccard, through associated matrices and how to use their eigenvectors to find the clustering of the graph into multiple partitions. We'll also show how to take advantage of efficient implementation of Lanczos and LOBPCG eigenvalue solvers and k-means algorithm on the GPU to compute clustering using our general framework. Finally, we'll highlight the performance and quality of our approach versus existing state-of-the-art techniques.
 
Topics:
Accelerated Data Science, Algorithms & Numerical Techniques, HPC and Supercomputing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7241
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