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 …

In this paper, we study the generalization capabilities of geometric graph neural networks
(GNNs). We consider GNNs over a geometric graph constructed from a finite set of randomly …

We study the generalization capabilities of Message Passing Neural Networks (MPNNs), a
prevalent class of Graph Neural Networks (GNN). We derive generalization bounds …

Training Graph Neural Networks (GNNs) on large graphs presents unique challenges due to
the large memory and computing requirements. Distributed GNN training, where the graph is …

We study the convergence of message passing graph neural networks on random graph
models to their continuous counterpart as the number of nodes tends to infinity. Until now …

Graph neural networks (GNNs) have become powerful tools for processing graph-based
information in various domains. A desirable property of GNNs is transferability, where a …

We propose a theoretical framework for training Graph Neural Networks (GNNs) on large
input graphs via training on small, fixed-size sampled subgraphs. This framework is …

Convolutional neural networks have been successfully extended to operate on graphs,
giving rise to Graph Neural Networks (GNNs). GNNs combine information from adjacent …

Large-scale graph machine learning is challenging as the complexity of learning models
scales with the graph size. Subsampling the graph is a viable alternative, but sampling on …

This paper addresses the problem of online network topology inference for expanding
graphs from a stream of spatiotemporal signals. Online algorithms for dynamic graph …