3 papers
stat.ML2026
-Nearest Neighbors in Gromov--Wasserstein Space
Kaitlyn Hohmeier, Nicolas Fraiman, Caroline Moosmueller
The Gromov--Wasserstein (GW) distance provides a framework for comparing metric measure spaces, regardless of their underlying structure or geometry. For network-based data, it ena…
math.ST2026
Exact recovery for seeded graph matching
Nicolas Fraiman, Michael Nisenzon
We study graph matching between two correlated networks in the almost fully seeded regime, where all but a vanishing fraction of vertex correspondences are revealed. Concretely, we…
math.ST2024
Semi-Supervised Community Detection via Quasi-Stationary Distributions
Nicolas Fraiman, Michael Nisenzon
Spectral clustering is a widely used method for community detection in networks. We focus on a semi-supervised community detection scenario in the Partially Labeled Stochastic Bloc…