196 citations · 359 across the 21 of their papers we have counts for
14 papers · 1 filter
Geometric Graph Filters and Neural Networks: Limit Properties and Discriminability Trade-offs
Zhiyang Wang, Luana Ruiz, Alejandro Ribeiro
This paper studies the relationship between a graph neural network (GNN) and a manifold neural network (MNN) when the graph is constructed from a set of points sampled from the man…
Graph Neural Tangent Kernel: Convergence on Large Graphs
Sanjukta Krishnagopal, Luana Ruiz
Graph neural networks (GNNs) achieve remarkable performance in graph machine learning tasks but can be hard to train on large-graph data, where their learning dynamics are not well…
Training Graph Neural Networks on Growing Stochastic Graphs
Juan Cervino, Luana Ruiz, Alejandro Ribeiro
Graph Neural Networks (GNNs) rely on graph convolutions to exploit meaningful patterns in networked data. Based on matrix multiplications, convolutions incur in high computational…
Transferability Properties of Graph Neural Networks
Luana Ruiz, Luiz F. O. Chamon, Alejandro Ribeiro
Graph neural networks (GNNs) are composed of layers consisting of graph convolutions and pointwise nonlinearities. Due to their invariance and stability properties, GNNs are provab…
Iterative Decoding for Compositional Generalization in Transformers
Luana Ruiz, Joshua Ainslie, Santiago Ontañón
Deep learning models generalize well to in-distribution data but struggle to generalize compositionally, i.e., to combine a set of learned primitives to solve more complex tasks. I…
Training Stable Graph Neural Networks Through Constrained Learning
Juan Cervino, Luana Ruiz, Alejandro Ribeiro
Graph Neural Networks (GNN) rely on graph convolutions to learn features from network data. GNNs are stable to different types of perturbations of the underlying graph, a property…
Stability to Deformations of Manifold Filters and Manifold Neural Networks
Zhiyang Wang, Luana Ruiz, Alejandro Ribeiro
The paper defines and studies manifold (M) convolutional filters and neural networks (NNs). \emph{Manifold} filters and MNNs are defined in terms of the Laplace-Beltrami operator e…
Learning by Transference: Training Graph Neural Networks on Growing Graphs
Juan Cervino, Luana Ruiz, Alejandro Ribeiro
Graph neural networks (GNNs) use graph convolutions to exploit network invariances and learn meaningful feature representations from network data. However, on large-scale graphs co…
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 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…
Gated Graph Convolutional Recurrent Neural Networks
Luana Ruiz, Fernando Gama, Alejandro Ribeiro
Graph processes model a number of important problems such as identifying the epicenter of an earthquake or predicting weather. In this paper, we propose a Graph Convolutional Recur…
Median activation functions for graph neural networks
Luana Ruiz, Fernando Gama, Antonio G. Marques +1
Graph neural networks (GNNs) have been shown to replicate convolutional neural networks' (CNNs) superior performance in many problems involving graphs. By replacing regular convolu…