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2017 GTC San Jose

S7241 - Spectral Clustering of Large Networks

Session Speakers
Session Description

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.


Additional Session Information
Intermediate
Talk
Accelerated Analytics Algorithms HPC and Supercomputing
Software
25 minutes
Session Schedule