196 citations · 359 across the 21 of their papers we have counts for
27 papers
Reply to 'Comments on Graphon Signal Processing' [arXiv:2310.14683]
Luana Ruiz, Luiz F. O. Chamon, Alejandro Ribeiro
This technical note addresses an issue [arXiv:2310.14683] with the proof (but not the statement) of [arXiv:2003.05030, Proposition 4]. The statement of the proposition is correct,…
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…
Convolutional Filtering on Sampled Manifolds
Zhiyang Wang, Luana Ruiz, Alejandro Ribeiro
The increasing availability of geometric data has motivated the need for information processing over non-Euclidean domains modeled as manifolds. The building block for information…
A Spectral Analysis of Graph Neural Networks on Dense and Sparse Graphs
Luana Ruiz, Ningyuan Huang, Soledad Villar
In this work we propose a random graph model that can produce graphs at different levels of sparsity. We analyze how sparsity affects the graph spectra, and thus the performance of…
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…
Convolutional Neural Networks on Manifolds: From Graphs and Back
Zhiyang Wang, Luana Ruiz, Alejandro Ribeiro
Geometric deep learning has gained much attention in recent years due to more available data acquired from non-Euclidean domains. Some examples include point clouds for 3D models a…
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…
Stable and Transferable Wireless Resource Allocation Policies via Manifold Neural Networks
Zhiyang Wang, Luana Ruiz, Mark Eisen +1
We consider the problem of resource allocation in large scale wireless networks. When contextualizing wireless network structures as graphs, we can model the limits of very large w…
Stability of Neural Networks on Manifolds to Relative Perturbations
Zhiyang Wang, Luana Ruiz, Alejandro Ribeiro
Graph Neural Networks (GNNs) show impressive performance in many practical scenarios, which can be largely attributed to their stability properties. Empirically, GNNs can scale wel…
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…
Stability of Neural Networks on Riemannian Manifolds
Zhiyang Wang, Luana Ruiz, Alejandro Ribeiro
Convolutional Neural Networks (CNNs) have been applied to data with underlying non-Euclidean structures and have achieved impressive successes. This brings the stability analysis o…
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…
Graphon Signal Processing
Luana Ruiz, Luiz F. O. Chamon, Alejandro Ribeiro
Graphons are infinite-dimensional objects that represent the limit of convergent sequences of graphs as their number of nodes goes to infinity. This paper derives a theory of graph…
Gated Graph Recurrent Neural Networks
Luana Ruiz, Fernando Gama, Alejandro Ribeiro
Graph processes exhibit a temporal structure determined by the sequence index and and a spatial structure determined by the graph support. To learn from graph processes, an informa…
The Graphon Fourier Transform
Luana Ruiz, Luiz F. O. Chamon, Alejandro Ribeiro
In many network problems, graphs may change by the addition of nodes, or the same problem may need to be solved in multiple similar graphs. This generates inefficiency, as analyses…