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05/29/2023
Geometric Graph Filters and Neural Networks: Limit Properties and Discriminability Trade-offs
This paper studies the relationship between a graph neural network (GNN)...
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01/25/2023
Graph Neural Tangent Kernel: Convergence on Large Graphs
Graph neural networks (GNNs) achieve remarkable performance in graph mac...
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11/20/2022
Convolutional Filtering on Sampled Manifolds
The increasing availability of geometric data has motivated the need for...
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11/06/2022
Graph Neural Networks for Community Detection on Sparse Graphs
Spectral methods provide consistent estimators for community detection i...
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10/27/2022
Training Graph Neural Networks on Growing Stochastic Graphs
Graph Neural Networks (GNNs) rely on graph convolutions to exploit meani...
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10/01/2022
Convolutional Neural Networks on Manifolds: From Graphs and Back
Geometric deep learning has gained much attention in recent years due to...
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12/09/2021
Transferability Properties of Graph Neural Networks
Graph neural networks (GNNs) are deep convolutional architectures consis...
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10/10/2021
Stability of Neural Networks on Manifolds to Relative Perturbations
Graph Neural Networks (GNNs) show impressive performance in many practic...
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10/08/2021
Iterative Decoding for Compositional Generalization in Transformers
Deep learning models do well at generalizing to in-distribution data but...
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10/07/2021
Training Stable Graph Neural Networks Through Constrained Learning
Graph Neural Networks (GNN) rely on graph convolutions to learn features...
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06/07/2021
Stability of Manifold Neural Networks to Deformations
Stability is an important property of graph neural networks (GNNs) which...
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06/07/2021
Increase and Conquer: Training Graph Neural Networks on Growing Graphs
Graph neural networks (GNNs) use graph convolutions to exploit network i...
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10/27/2020
Nonlinear State-Space Generalizations of Graph Convolutional Neural Networks
Graph convolutional neural networks (GCNNs) learn compositional represen...
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10/23/2020
Graph and graphon neural network stability
Graph neural networks (GNNs) are learning architectures that rely on kno...
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08/04/2020
Graph Neural Networks: Architectures, Stability and Transferability
Graph Neural Networks (GNNs) are information processing architectures fo...
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06/05/2020
Graphon Neural Networks and the Transferability of Graph Neural Networks
Graph neural networks (GNNs) rely on graph convolutions to extract local...
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03/03/2020
Graphon Pooling in Graph Neural Networks
Graph neural networks (GNNs) have been used effectively in different app...
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02/03/2020
Gated Graph Recurrent Neural Networks
Graph processes exhibit a temporal structure determined by the sequence ...
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03/29/2019
Invariance-Preserving Localized Activation Functions for Graph Neural Networks
Graph signals are signals with an irregular structure that can be descri...
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03/05/2019
Gated Graph Convolutional Recurrent Neural Networks
Graph processes model a number of important problems such as identifying...
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10/29/2018
