Subgraph GNNs are provably expressive neural architectures that learn graph
representations from sets of subgraphs. Unfortunately, their applicability is hampered by the …

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 transformers have recently received significant attention in graph learning, partly due
to their ability to capture more global interaction via self-attention. Nevertheless, while higher …

Message passing neural networks (MPNNs) have emerged as the most popular framework
of graph neural networks (GNNs) in recent years. However, their expressive power is limited …

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

This study addresses the limitations of the traditional analysis of message-passing, central to
graph learning, by defining {\em\textbf {generalized propagation}} with directed and …

Graph Neural Networks (GNN) have emerged as a popular and standard approach for
learning from graph-structured data. The literature on GNN highlights the potential of this …

To address the challenge of detecting small targets caused by the small size and high
quantity of targets in current unmanned aerial vehicle (UAV) aerial images, we propose a …

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

Graphs are fundamental mathematical abstractions, accurately modelling real-world
phenomena such as disease propagation, infrastructure organisation, and biological …