Title : Réseaux de Neurones en Graphes (GNN).
Although theorised about fifteen years ago, the scientific community’s interest for graph neural networks has only really taken off recently. Those models aim to transpose the representation learning capacity inherent in deep neural network onto graph data, via the learning of hidden states associated with the graph nodes. These hidden states are computed and updated according to the information contained in the neighborhoud of each node. In this presentation, we intend to make an overview of the different strategies of GNNs, namely spatial and spectral GNNs, with their advantages and weaknesses. This will lead to different propositions of models, on one side in order to reduce the number of parameters, and on the other side to include the edge attributes.
Guillaume Renton obtained his PhD from the University of Rouen in 2021. Since 2022, he is an assistant professor in the Multimedia Indexing and Data Integration (MIDI) team of ETIS at ENSEA. His domain of interest is on Machine Learning applied on structured data represented as graphs. A particular interest is given to the analysis and application of deep learning methods dedicated to graphs, called Graph Neural Networks.
Onsite and online (https://cnrs.zoom.us/j/92348178791?pwd=M0tCUE9OQUdPRmJDVVJqUlJDN1p3dz09)