Spectral clustering in the Gaussian mixture block model
The episode discusses Shuangping Li and Tselil Schramm's paper, "Spectral clustering in the Gaussian mixture block model." The hosts explore how spectral clustering performs on realistic high-dimensional networks where features are drawn from a mixture of two clusters. They detail the three main results, including embedding recovery, hypothesis testing, and clustering accuracy under specific conditions.