1 code implementation • 11 Dec 2023 • Victor M. Tenorio, Samuel Rey, Antonio G. Marques
Graph Neural Networks (GNNs) have emerged as a notorious alternative to address learning problems dealing with non-Euclidean datasets.
no code implementations • 16 Sep 2023 • Victor M. Tenorio, Samuel Rey, Antonio G. Marques
Blind deconvolution over graphs involves using (observed) output graph signals to obtain both the inputs (sources) as well as the filter that drives (models) the graph diffusion process.
no code implementations • 16 Sep 2023 • Victor M. Tenorio, Madeline Navarro, Santiago Segarra, Antonio G. Marques
We present a framework to recover completely missing node features for a set of graphs, where we only know the signals of a subset of graphs.
1 code implementation • 16 Oct 2022 • Samuel Rey, Victor M. Tenorio, Antonio G. Marques
Different from existing works, we formulate a non-convex optimization problem that operates in the vertex domain and jointly performs GF identification and graph denoising.
1 code implementation • 2 Oct 2021 • Victor M. Tenorio, Samuel Rey, Fernando Gama, Santiago Segarra, Antonio G. Marques
Graph convolutional neural networks (GCNNs) are popular deep learning architectures that, upon replacing regular convolutions with graph filters (GFs), generalize CNNs to irregular domains.