268 citations · 1.1k across the 58 of their papers we have counts for
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Nonlinear State-Space Generalizations of Graph Convolutional Neural Networks
Luana Ruiz, Fernando Gama, Alejandro Ribeiro +1
Graph convolutional neural networks (GCNNs) learn compositional representations from network data by nesting linear graph convolutions into nonlinearities. In this work, we approac…
Node-Adaptive Regularization for Graph Signal Reconstruction
Maosheng Yang, Mario Coutino, Geert Leus +1
A critical task in graph signal processing is to estimate the true signal from noisy observations over a subset of nodes, also known as the reconstruction problem. In this paper, w…
Online Time-Varying Topology Identification via Prediction-Correction Algorithms
Alberto Natali, Mario Coutino, Elvin Isufi +1
Signal processing and machine learning algorithms for data supported over graphs, require the knowledge of the graph topology. Unless this information is given by the physics of th…
Graph-Adaptive Activation Functions for Graph Neural Networks
Bianca Iancu, Luana Ruiz, Alejandro Ribeiro +1
Activation functions are crucial in graph neural networks (GNNs) as they allow defining a nonlinear family of functions to capture the relationship between the input graph data and…
Stochastic Graph Neural Networks
Zhan Gao, Elvin Isufi, Alejandro Ribeiro
Graph neural networks (GNNs) model nonlinear representations in graph data with applications in distributed agent coordination, control, and planning among others. Current GNN arch…
Forecasting Multi-Dimensional Processes over Graphs
Alberto Natali, Elvin Isufi, Geert Leus
The forecasting of multi-variate time processes through graph-based techniques has recently been addressed under the graph signal processing framework. However, problems in the rep…