no code implementations • 2 May 2024 • Alessio Gravina, Moshe Eliasof, Claudio Gallicchio, Davide Bacciu, Carola-Bibiane Schönlieb
A common problem in Message-Passing Neural Networks is oversquashing -- the limited ability to facilitate effective information flow between distant nodes.
1 code implementation • 30 Apr 2024 • Alessio Gravina, Daniele Zambon, Davide Bacciu, Cesare Alippi
Modern graph representation learning works mostly under the assumption of dealing with regularly sampled temporal graph snapshots, which is far from realistic, e. g., social networks and physical systems are characterized by continuous dynamics and sporadic observations.
1 code implementation • 12 Jul 2023 • Alessio Gravina, Davide Bacciu
Recent progress in research on Deep Graph Networks (DGNs) has led to a maturation of the domain of learning on graphs.
1 code implementation • 18 Oct 2022 • Alessio Gravina, Davide Bacciu, Claudio Gallicchio
Deep Graph Networks (DGNs) currently dominate the research landscape of learning from graphs, due to their efficiency and ability to implement an adaptive message-passing scheme between the nodes.