This paper studies the problem of matching two complete graphs with edge weights
correlated through latent geometries, extending a recent line of research on random graph …

We study one-way matching of a pair of datasets with low rank signals. Under a stylized
model, we first derive information-theoretic limits of matching under a mismatch proportion …

Motivated by applications to group synchronization and quadratic assignment on random
data, we study a general problem of Bayesian inference of an unknown``signal''belonging to …

We characterize the performance of graph neural networks for graph alignment problems in
the presence of vertex feature information. More specifically, given two graphs that are …

Motivated by the problem of matching two correlated random geometric graphs, we study the
problem of matching two Gaussian geometric models correlated through a latent node …

We study the problem of reconstructing a perfect matching M∗ hidden in a randomly
weighted n× n bipartite graph. The edge set includes every node pair in M∗ and each of the …

This paper considers the problem of detecting whether two databases, each consisting of n
users with d Gaussian features, are correlated. Under the null hypothesis, the databases are …

The re-identification or de-anonymization of users from anonymized data through matching
with publicly-available correlated user data has raised privacy concerns, leading to the …

In this work, we propose an efficient statistical test solving a problem of correlation detection
between two Gaussian databases. Correlation detection is a hypothesis testing problem; …

Matching entries of correlated shuffled databases have practical applications ranging from
privacy to biology. In this paper, motivated by synchronization errors in the sampling of time …