31 citations · 55 across the 5 of their papers we have counts for
3 papers · 1 filter
CIN++: Enhancing Topological Message Passing
Lorenzo Giusti, Teodora Reu, Francesco Ceccarelli +2
Graph Neural Networks (GNNs) have demonstrated remarkable success in learning from graph-structured data. However, they face significant limitations in expressive power, struggling…
On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology
Francesco Di Giovanni, Lorenzo Giusti, Federico Barbero +3
Message Passing Neural Networks (MPNNs) are instances of Graph Neural Networks that leverage the graph to send messages over the edges. This inductive bias leads to a phenomenon kn…
Cell Attention Networks
Lorenzo Giusti, Claudio Battiloro, Lucia Testa +3
Since their introduction, graph attention networks achieved outstanding results in graph representation learning tasks. However, these networks consider only pairwise relationships…