4 citations · 10 across the 13 of their papers we have counts for
8 papers · 1 filter
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…
Graph and graphon neural network stability
Luana Ruiz, Zhiyang Wang, Alejandro Ribeiro
Graph neural networks (GNNs) are learning architectures that rely on knowledge of the graph structure to generate meaningful representations of large-scale network data. GNN stabil…
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…
Graph Neural Networks: Architectures, Stability and Transferability
Luana Ruiz, Fernando Gama, Alejandro Ribeiro
Graph Neural Networks (GNNs) are information processing architectures for signals supported on graphs. They are presented here as generalizations of convolutional neural networks (…
Graphon Neural Networks and the Transferability of Graph Neural Networks
Luana Ruiz, Luiz F. O. Chamon, Alejandro Ribeiro
Graph neural networks (GNNs) rely on graph convolutions to extract local features from network data. These graph convolutions combine information from adjacent nodes using coeffici…
Graphon Pooling in Graph Neural Networks
Alejandro Parada-Mayorga, Luana Ruiz, Alejandro Ribeiro
Graph neural networks (GNNs) have been used effectively in different applications involving the processing of signals on irregular structures modeled by graphs. Relying on the use…