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ORCIDFrancesco Di Giovanni
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2020 – today
- 2024
[j2]Francesco Di Giovanni, T. Konstantin Rusch, Michael M. Bronstein, Andreea Deac, Marc Lackenby, Siddhartha Mishra, Petar Velickovic:
How does over-squashing affect the power of GNNs? Trans. Mach. Learn. Res. 2024 (2024)- [c8]Federico Barbero, Ameya Velingker, Amin Saberi, Michael M. Bronstein, Francesco Di Giovanni:
Locality-Aware Graph Rewiring in GNNs. ICLR 2024 - [i14]Joshua Southern, Francesco Di Giovanni, Michael M. Bronstein, Johannes F. Lutzeyer:
Understanding Virtual Nodes: Oversmoothing, Oversquashing, and Node Heterogeneity. CoRR abs/2405.13526 (2024) - [i13]Nian Liu, Xiaoxin He, Thomas Laurent, Francesco Di Giovanni, Michael M. Bronstein, Xavier Bresson:
Advancing Graph Convolutional Networks via General Spectral Wavelets. CoRR abs/2405.13806 (2024) - [i12]Kacper Kapusniak, Peter Potaptchik, Teodora Reu, Leo Zhang, Alexander Tong, Michael M. Bronstein, Avishek Joey Bose, Francesco Di Giovanni:
Metric Flow Matching for Smooth Interpolations on the Data Manifold. CoRR abs/2405.14780 (2024) - 2023
- [j1]Francesco Di Giovanni, James Rowbottom, Benjamin Paul Chamberlain, Thomas Markovich, Michael M. Bronstein:
Understanding convolution on graphs via energies. Trans. Mach. Learn. Res. 2023 (2023) - [c7]Francesco Di Giovanni, Lorenzo Giusti, Federico Barbero, Giulia Luise, Pietro Lio, Michael M. Bronstein:
On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology. ICML 2023: 7865-7885 - [c6]Benjamin Gutteridge, Xiaowen Dong, Michael M. Bronstein, Francesco Di Giovanni:
DRew: Dynamically Rewired Message Passing with Delay. ICML 2023: 12252-12267 - [c5]Emanuele Rossi, Bertrand Charpentier, Francesco Di Giovanni, Fabrizio Frasca, Stephan Günnemann, Michael M. Bronstein:
Edge Directionality Improves Learning on Heterophilic Graphs. LoG 2023: 25 - [c4]Floor Eijkelboom, Erik J. Bekkers, Michael M. Bronstein, Francesco Di Giovanni:
Can strong structural encoding reduce the importance of Message Passing? TAG-ML 2023: 278-288 - [i11]Francesco Di Giovanni, Lorenzo Giusti, Federico Barbero, Giulia Luise, Pietro Lio', Michael M. Bronstein:
On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology. CoRR abs/2302.02941 (2023) - [i10]Benjamin Gutteridge, Xiaowen Dong, Michael M. Bronstein, Francesco Di Giovanni:
DRew: Dynamically Rewired Message Passing with Delay. CoRR abs/2305.08018 (2023) - [i9]Emanuele Rossi, Bertrand Charpentier, Francesco Di Giovanni, Fabrizio Frasca, Stephan Günnemann, Michael M. Bronstein:
Edge Directionality Improves Learning on Heterophilic Graphs. CoRR abs/2305.10498 (2023) - [i8]Francesco Di Giovanni, T. Konstantin Rusch, Michael M. Bronstein, Andreea Deac, Marc Lackenby, Siddhartha Mishra, Petar Velickovic:
How does over-squashing affect the power of GNNs? CoRR abs/2306.03589 (2023) - [i7]Federico Barbero, Ameya Velingker, Amin Saberi, Michael M. Bronstein, Francesco Di Giovanni:
Locality-Aware Graph-Rewiring in GNNs. CoRR abs/2310.01668 (2023) - [i6]Floor Eijkelboom, Erik J. Bekkers, Michael M. Bronstein, Francesco Di Giovanni:
Can strong structural encoding reduce the importance of Message Passing? CoRR abs/2310.15197 (2023) - 2022
- [c3]Jake Topping, Francesco Di Giovanni, Benjamin Paul Chamberlain, Xiaowen Dong, Michael M. Bronstein:
Understanding over-squashing and bottlenecks on graphs via curvature. ICLR 2022 - [c2]Cristian Bodnar, Francesco Di Giovanni, Benjamin Paul Chamberlain, Pietro Lió, Michael M. Bronstein:
Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs. NeurIPS 2022 - [i5]Francesco Di Giovanni, Giulia Luise, Michael M. Bronstein:
Heterogeneous manifolds for curvature-aware graph embedding. CoRR abs/2202.01185 (2022) - [i4]Cristian Bodnar, Francesco Di Giovanni, Benjamin Paul Chamberlain, Pietro Liò, Michael M. Bronstein:
Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs. CoRR abs/2202.04579 (2022) - [i3]Francesco Di Giovanni, James Rowbottom, Benjamin Paul Chamberlain, Thomas Markovich, Michael M. Bronstein:
Graph Neural Networks as Gradient Flows. CoRR abs/2206.10991 (2022) - 2021
- [c1]Ben Chamberlain, James Rowbottom, Davide Eynard, Francesco Di Giovanni, Xiaowen Dong, Michael M. Bronstein:
Beltrami Flow and Neural Diffusion on Graphs. NeurIPS 2021: 1594-1609 - [i2]Benjamin Paul Chamberlain, James Rowbottom, Davide Eynard, Francesco Di Giovanni, Xiaowen Dong, Michael M. Bronstein:
Beltrami Flow and Neural Diffusion on Graphs. CoRR abs/2110.09443 (2021) - [i1]Jake Topping, Francesco Di Giovanni, Benjamin Paul Chamberlain, Xiaowen Dong, Michael M. Bronstein:
Understanding over-squashing and bottlenecks on graphs via curvature. CoRR abs/2111.14522 (2021)
Coauthor Index
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