385 citations · 1.8k across the 36 of their papers we have counts for
20 papers · 1 filter
Joint Network Topology Inference in the Presence of Hidden Nodes
Madeline Navarro, Samuel Rey, Andrei Buciulea +2
We investigate the increasingly prominent task of jointly inferring multiple networks from nodal observations. While most joint inference methods assume that observations are avail…
Graph Signal Processing: History, Development, Impact, and Outlook
Geert Leus, Antonio G. Marques, José M. F. Moura +2
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. Graphs are ve…
Graph Learning from Gaussian and Stationary Graph Signals
Andrei Buciulea, Antonio G. Marques
Graphs have become pervasive tools to represent information and datasets with irregular support. However, in many cases, the underlying graph is either unavailable or naively obtai…
Joint graph learning from Gaussian observations in the presence of hidden nodes
Samuel Rey, Madeline Navarro, Andrei Buciulea +2
Graph learning problems are typically approached by focusing on learning the topology of a single graph when signals from all nodes are available. However, many contemporary setups…
Robust Graph Filter Identification and Graph Denoising from Signal Observations
Samuel Rey, Victor M. Tenorio, Antonio G. Marques
When facing graph signal processing tasks, the workhorse assumption is that the graph describing the support of the signals is known. However, in many relevant applications the ava…
Enhanced graph-learning schemes driven by similar distributions of motifs
Samuel Rey, T. Mitchell Roddenberry, Santiago Segarra +1
This paper looks at the task of network topology inference, where the goal is to learn an unknown graph from nodal observations. One of the novelties of the approach put forth is t…
A Robust Alternative for Graph Convolutional Neural Networks via Graph Neighborhood Filters
Victor M. Tenorio, Samuel Rey, Fernando Gama +2
Graph convolutional neural networks (GCNNs) are popular deep learning architectures that, upon replacing regular convolutions with graph filters (GFs), generalize CNNs to irregular…
Untrained Graph Neural Networks for Denoising
Samuel Rey, Santiago Segarra, Reinhard Heckel +1
A fundamental problem in signal processing is to denoise a signal. While there are many well-performing methods for denoising signals defined on regular supports, such as images de…
Graph-signal Reconstruction and Blind Deconvolution for Structured Inputs
David Ramírez, Antonio G. Marques, Santiago Segarra
Key to successfully deal with complex contemporary datasets is the development of tractable models that account for the irregular structure of the information at hand. This paper p…
Robust graph-filter identification with graph denoising regularization
Samuel Rey, Antonio G. Marques
When approaching graph signal processing tasks, graphs are usually assumed to be perfectly known. However, in many practical applications, the observed (inferred) network is prone…
Blind Demixing of Diffused Graph Signals
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. A key aspect is how the data, repr…
Signal Processing on Directed Graphs
Antonio G. Marques, Santiago Segarra, Gonzalo Mateos
This paper provides an overview of the current landscape of signal processing (SP) on directed graphs (digraphs). Directionality is inherent to many real-world (information, transp…
Generative Adversarial Networks For Graph Data Imputation From Signed Observations
Amarlingam Madapu, Santiago Segarra, Sundeep Prabhakar Chepuri +1
We study the problem of missing data imputation for graph signals from signed one-bit quantized observations. More precisely, we consider that the true graph data is drawn from a d…
Estimating Network Processes via Blind Identification of Multiple Graph Filters
Yu Zhu, Fernando J. Iglesias, Antonio G. Marques +1
This paper studies the problem of jointly estimating multiple network processes driven by a common unknown input, thus effectively generalizing the classical blind multi-channel id…
An Underparametrized Deep Decoder Architecture for Graph Signals
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…
Invariance-Preserving Localized Activation Functions for Graph Neural Networks
Luana Ruiz, Fernando Gama, Antonio G. Marques +1
Graph signals are signals with an irregular structure that can be described by a graph. Graph neural networks (GNNs) are information processing architectures tailored to these grap…
Reinforcement Learning for Adaptive Caching with Dynamic Storage Pricing
Alireza Sadeghi, Fatemeh Sheikholeslami, Antonio G. Marques +1
Small base stations (SBs) of fifth-generation (5G) cellular networks are envisioned to have storage devices to locally serve requests for reusable and popular contents by \emph{cac…
Connecting the Dots: Identifying Network Structure via Graph Signal Processing
Gonzalo Mateos, Santiago Segarra, Antonio G. Marques +1
Network topology inference is a prominent problem in Network Science. Most graph signal processing (GSP) efforts to date assume that the underlying network is known, and then analy…
Convolutional Neural Network Architectures for Signals Supported on Graphs
Fernando Gama, Antonio G. Marques, Geert Leus +1
Two architectures that generalize convolutional neural networks (CNNs) for the processing of signals supported on graphs are introduced. We start with the selection graph neural ne…
Identifying the Topology of Undirected Networks from Diffused Non-stationary Graph Signals
Rasoul Shafipour, Santiago Segarra, Antonio G. Marques +1
We address the problem of inferring an undirected graph from nodal observations, which are modeled as non-stationary graph signals generated by local diffusion dynamics that depend…