Statistical Inference for Networks via Spectral Embedding
Dan Sussman, Harvard, Statistics
In this talk we will discuss using spectral methods for network analysis. We will show how a spectral embedding provides consistent estimates for latent positions in the random dot product graph model. This leads to accurate subsequent inference for a variety of tasks. An application to diffusion tensor MRI will be briefly discussed before overviewing some more practical aspects of spectral embedding.
September, 28 2015 | 12:30 p.m. - 2:00 p.m. | 230E Gross Hall