Search Results for author: Antonio G. Marques

Found 31 papers, 12 papers with code

Learning graphs and simplicial complexes from data

no code implementations16 Dec 2023 Andrei Buciulea, Elvin Isufi, Geert Leus, Antonio G. Marques

Graphs are widely used to represent complex information and signal domains with irregular support.

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

Joint Network Topology Inference in the Presence of Hidden Nodes

no code implementations30 Jun 2023 Madeline Navarro, Samuel Rey, Andrei Buciulea, Antonio G. Marques, Santiago Segarra

We investigate the increasingly prominent task of jointly inferring multiple networks from nodal observations.

Graph Signal Processing: History, Development, Impact, and Outlook

no code implementations21 Mar 2023 Geert Leus, Antonio G. Marques, José M. F. Moura, Antonio Ortega, David I Shuman

Graph signal processing (GSP) generalizes signal processing (SP) tasks to signals living on non-Euclidean domains whose structure can be captured by a weighted graph.

Graph Learning

Graph Learning from Gaussian and Stationary Graph Signals

no code implementations13 Mar 2023 Andrei Buciulea, Antonio G. Marques

Graphs have become pervasive tools to represent information and datasets with irregular support.

Graph Learning

Joint graph learning from Gaussian observations in the presence of hidden nodes

1 code implementation4 Dec 2022 Samuel Rey, Madeline Navarro, Andrei Buciulea, Santiago Segarra, Antonio G. Marques

Motivated by this, we propose a joint graph learning method that takes into account the presence of hidden (latent) variables.

Graph Learning Graph Similarity

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

Enhanced graph-learning schemes driven by similar distributions of motifs

no code implementations11 Jul 2022 Samuel Rey, T. Mitchell Roddenberry, Santiago Segarra, Antonio G. Marques

Guided by this, we first assume that we have a reference graph that is related to the sought graph (in the sense of having similar motif densities) and then, we exploit this relation by incorporating a similarity constraint and a regularization term in the network topology inference optimization problem.

Graph Learning Inference Optimization

Joint inference of multiple graphs with hidden variables from stationary graph signals

1 code implementation5 Oct 2021 Samuel Rey, Andrei Buciulea, Madeline Navarro, Santiago Segarra, Antonio G. Marques

Learning graphs from sets of nodal observations represents a prominent problem formally known as graph topology inference.

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

Untrained Graph Neural Networks for Denoising

1 code implementation24 Sep 2021 Samuel Rey, Santiago Segarra, Reinhard Heckel, Antonio G. Marques

This paper introduces two untrained graph neural network architectures for graph signal denoising, provides theoretical guarantees for their denoising capabilities in a simple setup, and numerically validates the theoretical results in more general scenarios.

Denoising

Graph-signal Reconstruction and Blind Deconvolution for Structured Inputs

no code implementations31 May 2021 David Ramírez, Antonio G. Marques, Santiago Segarra

When either the input or the filter coefficients are known, this is tantamount to assuming that the signals of interest live on a subspace defined by the supporting graph.

Low-rank State-action Value-function Approximation

1 code implementation18 Apr 2021 Sergio Rozada, Victor Tenorio, Antonio G. Marques

Value functions are central to Dynamic Programming and Reinforcement Learning but their exact estimation suffers from the curse of dimensionality, challenging the development of practical value-function (VF) estimation algorithms.

Q-Learning

Robust graph-filter identification with graph denoising regularization

1 code implementation10 Mar 2021 Samuel Rey, Antonio G. Marques

When approaching graph signal processing tasks, graphs are usually assumed to be perfectly known.

Denoising

Blind Demixing of Diffused Graph Signals

no code implementations24 Dec 2020 Fernando J. Iglesias Garcia, Santiago Segarra, Antonio G. Marques

Using graphs to model irregular information domains is an effective approach to deal with some of the intricacies of contemporary (network) data.

blind source separation

Joint Inference of Multiple Graphs from Matrix Polynomials

no code implementations16 Oct 2020 Madeline Navarro, Yuhao Wang, Antonio G. Marques, Caroline Uhler, Santiago Segarra

Inferring graph structure from observations on the nodes is an important and popular network science task.

Signal Processing on Directed Graphs

no code implementations2 Aug 2020 Antonio G. Marques, Santiago Segarra, Gonzalo Mateos

This paper provides an overview of the current landscape of signal processing (SP) on directed graphs (digraphs).

Causal Inference

Tensor Graph Convolutional Networks for Multi-relational and Robust Learning

no code implementations15 Mar 2020 Vassilis N. Ioannidis, Antonio G. Marques, Georgios B. Giannakis

The era of "data deluge" has sparked renewed interest in graph-based learning methods and their widespread applications ranging from sociology and biology to transportation and communications.

Sociology

An Underparametrized Deep Decoder Architecture for Graph Signals

1 code implementation2 Aug 2019 Samuel Rey, Antonio G. Marques, Santiago Segarra

While deep convolutional architectures have achieved remarkable results in a gamut of supervised applications dealing with images and speech, recent works show that deep untrained non-convolutional architectures can also outperform state-of-the-art methods in several tasks such as image compression and denoising.

Clustering Denoising +1

Invariance-Preserving Localized Activation Functions for Graph Neural Networks

no code implementations29 Mar 2019 Luana Ruiz, Fernando Gama, Antonio G. Marques, Alejandro Ribeiro

Graph neural networks (GNNs) are information processing architectures tailored to these graph signals and made of stacked layers that compose graph convolutional filters with nonlinear activation functions.

Authorship Attribution Recommendation Systems

Reinforcement Learning for Adaptive Caching with Dynamic Storage Pricing

no code implementations17 Dec 2018 Alireza Sadeghi, Fatemeh Sheikholeslami, Antonio G. Marques, Georgios B. Giannakis

Under this generic formulation, first by considering stationary distributions for the costs and file popularities, an efficient reinforcement learning-based solver known as value iteration algorithm can be used to solve the emerging optimization problem.

Decision Making Q-Learning +2

A Recurrent Graph Neural Network for Multi-Relational Data

1 code implementation5 Nov 2018 Vassilis N. Ioannidis, Antonio G. Marques, Georgios B. Giannakis

The era of data deluge has sparked the interest in graph-based learning methods in a number of disciplines such as sociology, biology, neuroscience, or engineering.

Sociology

Median activation functions for graph neural networks

no code implementations29 Oct 2018 Luana Ruiz, Fernando Gama, Antonio G. Marques, Alejandro Ribeiro

Graph neural networks (GNNs) have been shown to replicate convolutional neural networks' (CNNs) superior performance in many problems involving graphs.

MIMO Graph Filters for Convolutional Neural Networks

no code implementations6 Mar 2018 Fernando Gama, Antonio G. Marques, Alejandro Ribeiro, Geert Leus

Superior performance and ease of implementation have fostered the adoption of Convolutional Neural Networks (CNNs) for a wide array of inference and reconstruction tasks.

Convolutional Neural Networks Via Node-Varying Graph Filters

no code implementations27 Oct 2017 Fernando Gama, Geert Leus, Antonio G. Marques, Alejandro Ribeiro

Convolutional neural networks (CNNs) are being applied to an increasing number of problems and fields due to their superior performance in classification and regression tasks.

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