Designing expressive Graph Neural Networks (GNNs) is a fundamental topic in the graph
learning community. So far, GNN expressiveness has been primarily assessed via the …

Graph neural networks (GNNs) for link prediction can loosely be divided into two broad
categories. First, node-wise architectures pre-compute individual embeddings for each node …

Graph Neural Networks (GNNs) are extensively employed in graph machine learning, with
considerable research focusing on their expressiveness. Current studies often assess GNN …

Graph neural networks (GNNs) have achieved remarkable success in a variety of machine
learning tasks over graph data. Existing GNNs usually rely on message passing, ie …

The ability of graph neural networks (GNNs) to count homomorphisms has recently been
proposed as a practical and fine-grained measure of their expressive power. Although …

Recent advancements in graph learning have revolutionized the way to understand and
analyze data with complex structures. Notably, Graph Neural Networks (GNNs), ie neural …

We introduce $ r $-loopy Weisfeiler-Leman ($ r $-$\ell {} $ WL), a novel hierarchy of graph
isomorphism tests and a corresponding GNN framework, $ r $-$\ell {} $ MPNN, that can …

Multi-agent reinforcement learning presents unique hurdles such as the nonstationary
problem beyond single-agent reinforcement learning that makes learning effective …