Message-passing has proved to be an effective way to design graph neural networks, as it is
able to leverage both permutation equivariance and an inductive bias towards learning local …

A key requirement for graph neural networks is that they must process a graph in a way that
does not depend on how the graph is described. Traditionally this has been taken to mean …

Most graph neural network models rely on a particular message passing paradigm, where
the idea is to iteratively propagate node representations of a graph to each node in the direct …

Graph Neural Networks (GNNs) have shown promising potential in graph representation
learning. The majority of GNNs define a local message-passing mechanism, propagating …

We introduce Automorphism-based graph neural networks (Autobahn), a new family of
graph neural networks. In an Autobahn, we decompose the graph into a collection of …

Abstract Graph Neural Networks (GNNs) have become a prominent approach to machine
learning with graphs and have been increasingly applied in a multitude of domains …

Abstract Graph Neural Networks (GNNs) are a class of deep models that operates on data
with arbitrary topology and order-invariant structure represented as graphs. We introduce an …

We present a hierarchical neural message passing architecture for learning on molecular
graphs. Our model takes in two complementary graph representations: the raw molecular …

The message passing framework is the foundation of the immense success enjoyed by
graph neural networks (GNNs) in recent years. In spite of its elegance, there exist many …

Message Passing Neural Networks (MPNNs) are a common type of Graph Neural Network
(GNN), in which each node's representation is computed recursively by aggregating …