In recent years, algorithms and neural architectures based on the Weisfeiler-Leman
algorithm, a well-known heuristic for the graph isomorphism problem, have emerged as a …

This work presents a two-stage neural architecture for learning and refining structural
correspondences between graphs. First, we use localized node embeddings computed by a …

In recent years, algorithms and neural architectures based on the Weisfeiler-Leman
algorithm, a well-known heuristic for the graph isomorphism problem, emerged as a …

Entity resolution (ER) is the task of finding records that refer to the same real-world entities. A
common scenario, which we refer to as Clean-Clean ER, is to resolve records across two …

Scene graphs have been recently introduced into 3D spatial understanding as a
comprehensive representation of the scene. The alignment between 3D scene graphs is the …

In recent years, graph neural networks (GNNs) have emerged as a promising tool for solving
machine learning problems on graphs. Most GNNs are members of the family of message …

Abstract The classical Weisfeiler-Leman algorithm aka color refinement is fundamental for
graph learning with kernels and neural networks. Originally developed for graph …

Entity Resolution (ER) is the task of finding records that refer to the same real-world entities.
A common scenario is when entities across two clean sources need to be resolved, which …

The graph edit distance, used for comparing graphs in various domains, is often
approximated due to its high computational complexity. Widely used heuristics search for an …

Finding the graphs that are most similar to a query graph in a large database is a common
task with various applications. A widely-used similarity measure is the graph edit distance …