__Claremont Graduate University__ | __Claremont McKenna__ | __Harvey Mudd__ | __Pitzer__ | __Pomona__ | __Scripps__

Proudly Serving Math Community at the Claremont Colleges Since 2007

Copyright © 2011 Claremont Center for the Mathematical Sciences

10/27/2015 - 12:15pm

10/27/2015 - 1:10pm

Speaker:

Blake Hunter (CMC)

Abstract:

Expander graphs are widely used in Computer Science and Mathematics. A graph is expanding if the second eigenvalue of the standard random walk on this graph is bounded away from 1 (equivalently, the smallest eigenvalue of the Laplacian is strictly larger than 0). Graph partitioning has recently gained popularity in computer vision (clustering) and in network analysis (community detection) because if it’s ability to gain latent knowledge of a system given no prior information. Spectral clustering is a graph partitioning method that uses the eigenvector corresponding to the smallest eigenvalue of the graph Laplacian (or the second largest eigenvalue of the standard random walk). This talk will explore the surprising connections between expander graphs and graph partitioning.

Where:

Millikan 2099, Pomona College