Robust clustering and testing for large complex networks using rank statistics

Music City Center 

This talk presents new methods and theory for robust spectral clustering and hypothesis testing in large edge-weighted random graphs using rank statistics. The proposed approach brings together contemporary developments in spectral methods and classical developments in the theory of rank-based nonparametric tests. Unlike non-robust approaches, our methodology remains effective in the presence of outliers, heavy-tailed distributions, and heterogeneous noise variances. Applications to human connectome data are provided and suggest directions for future work.

Clustering

Robust statistics

Network analysis

Random graph

Nonparametric statistics