12 citations · 24 across the 5 of their papers we have counts for
5 papers
Technical report: Graph Neural Networks go Grammatical
Jason Piquenot, Aldo Moscatelli, Maxime Bérar +4
This paper introduces a framework for formally establishing a connection between a portion of an algebraic language and a Graph Neural Network (GNN). The framework leverages Contex…
Deep graph matching meets mixed-integer linear programming: Relax at your own risk ?
Zhoubo Xu, Puqing Chen, Romain Raveaux +2
Graph matching is an important problem that has received widespread attention, especially in the field of computer vision. Recently, state-of-the-art methods seek to incorporate gr…
On the unification of the graph edit distance and graph matching problems
Romain Raveaux
Error-tolerant graph matching gathers an important family of problems. These problems aim at finding correspondences between two graphs while integrating an error model. In the Gra…
A Convolutional Neural Network into graph space
Chloé Martineau, Romain Raveaux, Donatello Conte +1
Convolutional neural networks (CNNs), in a few decades, have outperformed the existing state of the art methods in classification context. However, in the way they were formalised,…
Graph edit distance : a new binary linear programming formulation
Julien Lerouge, Zeina Abu-Aisheh, Romain Raveaux +2
Graph edit distance (GED) is a powerful and flexible graph matching paradigm that can be used to address different tasks in structural pattern recognition, machine learning, and da…