8 papers
Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modeling
Andrea Ceni, Alessio Gravina, Claudio Gallicchio +3
The recent success of State-Space Models (SSMs) in sequence modeling has motivated their adaptation to graph learning, giving rise to Graph State-Space Models (GSSMs). However, exi…
Improving Long-Range Interactions in Graph Neural Simulators via Hamiltonian Dynamics
Tai Hoang, Alessandro Trenta, Alessio Gravina +4
Learning to simulate complex physical systems from data has emerged as a promising way to overcome the limitations of traditional numerical solvers, which often require prohibitive…
Return of ChebNet: Understanding and Improving an Overlooked GNN on Long Range Tasks
Ali Hariri, Ãlvaro Arroyo, Alessio Gravina +6
ChebNet, one of the earliest spectral GNNs, has largely been overshadowed by Message Passing Neural Networks (MPNNs), which gained popularity for their simplicity and effectiveness…
Non-Dissipative Graph Propagation for Non-Local Community Detection
William Leeney, Alessio Gravina, Davide Bacciu
Community detection in graphs aims to cluster nodes into meaningful groups, a task particularly challenging in heterophilic graphs, where nodes sharing similarities and membership…
ECLYPSE: a Python Framework for Simulation and Emulation of the Cloud-Edge Continuum
Jacopo Massa, Valerio De Caro, Stefano Forti +3
The Cloud-Edge continuum enhances application performance by bringing computation closer to data sources. However, it presents considerable challenges in managing resources and det…
On Oversquashing in Graph Neural Networks Through the Lens of Dynamical Systems
Alessio Gravina, Moshe Eliasof, Claudio Gallicchio +2
A common problem in Message-Passing Neural Networks is oversquashing -- the limited ability to facilitate effective information flow between distant nodes. Oversquashing is attribu…