Search Results for author: Victor M. Tenorio

Found 5 papers, 3 papers with code

Robust Graph Neural Network based on Graph Denoising

1 code implementation11 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.

Denoising Graph Learning

Blind Deconvolution of Sparse Graph Signals in the Presence of Perturbations

no code implementations16 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.

Recovering Missing Node Features with Local Structure-based Embeddings

no code implementations16 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.

Graph Classification

Robust Graph Filter Identification and Graph Denoising from Signal Observations

1 code implementation16 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.

Denoising

A Robust Alternative for Graph Convolutional Neural Networks via Graph Neighborhood Filters

1 code implementation2 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.

Denoising Node Classification

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