Training on large-scale graphs has achieved remarkable results in graph representation
learning, but its cost and storage have attracted increasing concerns. Existing graph …

Graph neural networks (GNNs) are information processing architectures for signals
supported on graphs. They are presented here as generalizations of convolutional neural …

Graph neural networks (GNNs) rely on graph convolutions to extract local features from
network data. These graph convolutions combine information from adjacent nodes using …

Numerous recent works have analyzed the expressive power of message-passing graph
neural networks (MPNNs), primarily utilizing combinatorial techniques such as the $1 …

We study properties of Graph Convolutional Networks (GCNs) by analyzing their behavior
on standard models of random graphs, where nodes are represented by random latent …

We present an approach for analyzing message passing graph neural networks (MPNNs)
based on an extension of graphon analysis to a so called graphon-signal analysis. A MPNN …

We study spectral graph convolutional neural networks (GCNNs), where filters are defined
as continuous functions of the graph shift operator (GSO) through functional calculus. A …

We aim to deepen the theoretical understanding of Graph Neural Networks (GNNs) on large
graphs, with a focus on their expressive power. Existing analyses relate this notion to the …

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 …

Graph convolutional learning has led to many exciting discoveries in diverse areas.
However, in some applications, traditional graphs are insufficient to capture the structure …