In this article we propose a pooling approach for convolutional information processing on
graphs relying on the theory of graphons and limits of dense graph sequences. We present …

In this paper we introduce and study the algebraic generalization of non commutative
convolutional neural networks. We leverage the theory of algebraic signal processing to …

In this paper we study the stability properties of aggregation graph neural networks (Agg-
GNNs) considering perturbations of the underlying graph. An Agg-GNN is a hybrid …

In this paper, we develop a generalized theory of convolutional signal processing and
neural networks for Reproducing Kernel Hilbert Spaces (RKHS). Leveraging the theory of …

In this paper we discuss the results recently published in [1] about algebraic signal models
(ASMs) based on non commutative algebras and their use in convolutional neural networks …

In this work, we study the properties of sampling sets on families of large graphs by
leveraging the theory of graphons and graph limits. To this end, we extend to graphon …