Artificial intelligence for graphs has achieved remarkable success in modelling complex
systems, ranging from dynamic networks in biology to interacting particle systems in physics …

Graph transformers have emerged as a promising architecture for a variety of graph learning
and representation tasks. Despite their successes, though, it remains challenging to scale …

Subgraph GNNs are a recent class of expressive Graph Neural Networks (GNNs) which
model graphs as collections of subgraphs. So far, the design space of possible Subgraph …

Abstract Graph Neural Networks (GNNs) are limited in their expressive power, struggle with
long-range interactions and lack a principled way to model higher-order structures. These …

While Graph Neural Networks (GNNs) have achieved remarkable results in a variety of
applications, recent studies exposed important shortcomings in their ability to capture the …

Graph Convolutional Networks (GCNs) have been drawing significant attention with the
power of representation learning on graphs. Unlike Convolutional Neural Networks (CNNs) …

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 …

In recent years, algorithms and neural architectures based on the Weisfeiler-Leman
algorithm, a well-known heuristic for the graph isomorphism problem, have emerged as a …

Despite the great achievements of Graph Neural Networks (GNNs) in graph learning,
conventional GNNs struggle to break through the upper limit of the expressiveness of first …

Graph neural networks (GNNs) have recently become the standard approach for learning
with graph-structured data. Prior work has shed light into their potential, but also their …