10 citations · 13 across the 8 of their papers we have counts for
13 papers · 1 filter
Learning Stable Graph Neural Networks via Spectral Regularization
Zhan Gao, Elvin Isufi
Stability of graph neural networks (GNNs) characterizes how GNNs react to graph perturbations and provides guarantees for architecture performance in noisy scenarios. This paper de…
Graph filtering over expanding graphs
Bishwadeep Das, Elvin Isufi
Our capacity to learn representations from data is related to our ability to design filters that can leverage their coupling with the underlying domain. Graph filters are one such…
Learning Expanding Graphs for Signal Interpolation
Bishwadeep Das, Elvin Isufi
Performing signal processing over graphs requires knowledge of the underlying fixed topology. However, graphs often grow in size with new nodes appearing over time, whose connectiv…
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…