4 papers
Graph alignment in sparse inhomogeneous models via self-overlap
Louis Vassaux
We develop a general framework for understanding when graph alignment is information-theoretically feasible in sparse inhomogeneous random graph models, by studying the set of vert…
Phase Transition in Convex Relaxations for Graph Alignment
Laurent Massoulié, Sushil Mahavir Varma, Louis Vassaux +1
We study the graph alignment problem for correlated Gaussian Orthogonal Ensemble (GOE) matrices, where the goal is to recover a hidden vertex permutation given two correlated symme…
Brownian behaviour of the Riemann zeta function around the critical line
Louis Vassaux
We establish a Brownian extension to Selberg's central limit theorem for the Riemann zeta function. This implies various limiting distributions for , including an analogue of th…
The feasibility of multi-graph alignment: a Bayesian approach
Louis Vassaux, Laurent Massoulié
We establish thresholds for the feasibility of random multi-graph alignment in two models. In the Gaussian model, we demonstrate an "all-or-nothing" phenomenon: above a critical th…