Luana Ruiz

Graphon Pooling in Graph Neural Networks

2020

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 of shift-invariant graph filters, GNNs extend the operation of …

Stability of Manifold Neural Networks to Deformations.

2021 · arXiv (Cornell University)

Stability is an important property of graph neural networks (GNNs) which explains their success in many problems of practical interest. Existing GNN stability results depend on the size of the graph, restricting applicability to graphs …

Transferability Properties of Graph Neural Networks

2021 · arXiv (Cornell University)

Graph neural networks (GNNs) are composed of layers consisting of graph convolutions and pointwise nonlinearities. Due to their invariance and stability properties, GNNs are provably successful at learning representations from data supported on moderate-scale graphs. …