38 citations · 84 across the 6 of their papers we have counts for
6 papers
Dynamic Graph Representation Learning with Neural Networks: A Survey
Leshanshui Yang, Sébastien Adam, Clément Chatelain
In recent years, Dynamic Graph (DG) representations have been increasingly used for modeling dynamic systems due to their ability to integrate both topological and temporal informa…
Breaking the Limits of Message Passing Graph Neural Networks
Muhammet Balcilar, Pierre Héroux, Benoit Gaüzère +3
Since the Message Passing (Graph) Neural Networks (MPNNs) have a linear complexity with respect to the number of nodes when applied to sparse graphs, they have been widely implemen…
Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks
Muhammet Balcilar, Guillaume Renton, Pierre Heroux +3
This paper aims at revisiting Graph Convolutional Neural Networks by bridging the gap between spectral and spatial design of graph convolutions. We theoretically demonstrate some e…
Neural Networks Regularization Through Class-wise Invariant Representation Learning
Soufiane Belharbi, Clément Chatelain, Romain Hérault +1
Training deep neural networks is known to require a large number of training samples. However, in many applications only few training samples are available. In this work, we tackle…
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…
Deep Neural Networks Regularization for Structured Output Prediction
Soufiane Belharbi, Romain Hérault, Clément Chatelain +1
A deep neural network model is a powerful framework for learning representations. Usually, it is used to learn the relation by exploiting the regularities in the input $x…