Francesco Di Giovanni

According to our database, Francesco Di Giovanni authored at least 16 papers between 2021 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2024

How does over-squashing affect the power of GNNs?

Francesco Di Giovanni, T. Konstantin Rusch, Michael M. Bronstein, Andreea Deac, Marc Lackenby, Siddhartha Mishra, Petar Velickovic

Trans. Mach. Learn. Res., 2024

Relaxed Equivariance via Multitask Learning.

Ahmed A. A. Elhag, T. Konstantin Rusch, Francesco Di Giovanni, Michael M. Bronstein

CoRR, 2024

Advancing Graph Convolutional Networks via General Spectral Wavelets.

Nian Liu, Xiaoxin He, Thomas Laurent, Francesco Di Giovanni, Michael M. Bronstein, Xavier Bresson

CoRR, 2024

Understanding Virtual Nodes: Oversmoothing, Oversquashing, and Node Heterogeneity.

Joshua Southern, Francesco Di Giovanni, Michael M. Bronstein, Johannes F. Lutzeyer

CoRR, 2024

Metric Flow Matching for Smooth Interpolations on the Data Manifold.

Kacper Kapusniak, Peter Potaptchik, Teodora Reu, Leo Zhang, Alexander Tong, Michael M. Bronstein, Avishek Joey Bose, Francesco Di Giovanni

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Locality-Aware Graph Rewiring in GNNs.

Federico Barbero, Ameya Velingker, Amin Saberi, Michael M. Bronstein, Francesco Di Giovanni

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Understanding convolution on graphs via energies.

Francesco Di Giovanni, James Rowbottom, Benjamin Paul Chamberlain, Thomas Markovich, Michael M. Bronstein

Trans. Mach. Learn. Res., 2023

Can strong structural encoding reduce the importance of Message Passing?

Floor Eijkelboom, Erik J. Bekkers, Michael M. Bronstein, Francesco Di Giovanni

Proceedings of the Topological, 2023

Edge Directionality Improves Learning on Heterophilic Graphs.

Emanuele Rossi, Bertrand Charpentier, Francesco Di Giovanni, Fabrizio Frasca, Stephan Günnemann, Michael M. Bronstein

Proceedings of the Learning on Graphs Conference, 27-30 November 2023, Virtual Event., 2023

DRew: Dynamically Rewired Message Passing with Delay.

Benjamin Gutteridge, Xiaowen Dong, Michael M. Bronstein, Francesco Di Giovanni

Proceedings of the International Conference on Machine Learning, 2023

On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology.

Francesco Di Giovanni, Lorenzo Giusti, Federico Barbero, Giulia Luise, Pietro Lio, Michael M. Bronstein

Proceedings of the International Conference on Machine Learning, 2023

2022

Graph Neural Networks as Gradient Flows.

Francesco Di Giovanni, James Rowbottom, Benjamin Paul Chamberlain, Thomas Markovich, Michael M. Bronstein

CoRR, 2022

Heterogeneous manifolds for curvature-aware graph embedding.

Francesco Di Giovanni, Giulia Luise, Michael M. Bronstein

CoRR, 2022

Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs.

Cristian Bodnar, Francesco Di Giovanni, Benjamin Paul Chamberlain, Pietro Lió, Michael M. Bronstein

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Understanding over-squashing and bottlenecks on graphs via curvature.

Jake Topping, Francesco Di Giovanni, Benjamin Paul Chamberlain, Xiaowen Dong, Michael M. Bronstein

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Beltrami Flow and Neural Diffusion on Graphs.

Ben Chamberlain, James Rowbottom, Davide Eynard, Francesco Di Giovanni, Xiaowen Dong, Michael M. Bronstein

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021