The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Problems involving multiple networks are prevalent in many scientific and other domains. In
particular, network alignment, or the task of identifying corresponding nodes in different …

Linking accounts of the same user across datasets--even when personally identifying
information is removed or unavailable--is an important open problem studied in many …

Random graph matching refers to recovering the underlying vertex correspondence
between two random graphs with correlated edges; a prominent example is when the two …

Graph matching, also known as network alignment, aims at recovering the latent vertex
correspondence between two unlabeled, edge-correlated weighted graphs. To tackle this …

This paper deals with the problem of graph matching or network alignment for Erdős–Rényi
graphs, which can be viewed as a noisy average-case version of the graph isomorphism …

We consider the problem of perfectly recovering the vertex correspondence between two
correlated Erdos-Renyi (ER) graphs. For a pair of correlated graphs on the same vertex set …

Matching recovery threshold for correlated random graphs Page 1 The Annals of Statistics
2023, Vol. 51, No. 4, 1718–1743 https://doi.org/10.1214/23-AOS2305 © Institute of …

We study a noisy graph isomorphism problem, where the goal is to perfectly recover the
vertex correspondence between two edge‐correlated graphs, with an initial seed set of …

In this paper, we consider the graph alignment problem, which is the problem of recovering,
given two graphs, a one-to-one mapping between nodes that maximizes edge overlap. This …