31 citations · 33 across the 4 of their papers we have counts for
4 papers
Topological Neural Networks: Mitigating the Bottlenecks of Graph Neural Networks via Higher-Order Interactions
Lorenzo Giusti
The irreducible complexity of natural phenomena has led Graph Neural Networks to be employed as a standard model to perform representation learning tasks on graph-structured data.…
Neural Embeddings for Protein Graphs
Francesco Ceccarelli, Lorenzo Giusti, Sean B. Holden +1
Proteins perform much of the work in living organisms, and consequently the development of efficient computational methods for protein representation is essential for advancing lar…
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…