Beltrami Flow and Neural Diffusion on Graphs. - Ben Chamberlain 001; James Rowbottom; Davide Eynard; Francesco Di Giovanni; Xiaowen Dong 001; Michael M. Bronstein (NeurIPS, 2021)

Understanding over-squashing and bottlenecks on graphs via curvature. - Jake Topping; Francesco Di Giovanni; Benjamin Paul Chamberlain; Xiaowen Dong 001; Michael M. Bronstein (ICLR, 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 (NeurIPS, 2022)

Heterogeneous manifolds for curvature-aware graph embedding. - Francesco Di Giovanni; Giulia Luise; Michael M. Bronstein (CoRR, 2022)

Graph Neural Networks as Gradient Flows. - Francesco Di Giovanni; James Rowbottom; Benjamin Paul Chamberlain; Thomas Markovich; Michael M. Bronstein (CoRR, 2022)

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 (ICML, 2023)

DRew: Dynamically Rewired Message Passing with Delay. - Benjamin Gutteridge; Xiaowen Dong 001; Michael M. Bronstein; Francesco Di Giovanni (ICML, 2023)

Edge Directionality Improves Learning on Heterophilic Graphs. - Emanuele Rossi; Bertrand Charpentier; Francesco Di Giovanni; Fabrizio Frasca; Stephan Günnemann; Michael M. Bronstein (LoG, 2023)

Can strong structural encoding reduce the importance of Message Passing? - Floor Eijkelboom; Erik J. Bekkers; Michael M. Bronstein; Francesco Di Giovanni (TAG-ML, 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)

Locality-Aware Graph Rewiring in GNNs. - Federico Barbero; Ameya Velingker; Amin Saberi; Michael M. Bronstein; Francesco Di Giovanni (ICLR, 2024)

Metric Flow Matching for Smooth Interpolations on the Data Manifold. - Kacper Kapusniak; Peter Potaptchik; Teodora Reu; Leo Zhang; Alexander Tong 001; Michael M. Bronstein; Avishek Joey Bose; Francesco Di Giovanni (NeurIPS, 2024)

Understanding Virtual Nodes: Oversmoothing, Oversquashing, and Node Heterogeneity. - Joshua Southern; Francesco Di Giovanni; Michael M. Bronstein; Johannes F. Lutzeyer (CoRR, 2024)

Advancing Graph Convolutional Networks via General Spectral Wavelets. - Nian Liu 001; Xiaoxin He; Thomas Laurent 001; Francesco Di Giovanni; Michael M. Bronstein; Xavier Bresson (CoRR, 2024)

Relaxed Equivariance via Multitask Learning. - Ahmed A. A. Elhag; T. Konstantin Rusch; Francesco Di Giovanni; Michael M. Bronstein (CoRR, 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)

Understanding Virtual Nodes: Oversquashing and Node Heterogeneity. - Joshua Southern; Francesco Di Giovanni; Michael M. Bronstein; Johannes F. Lutzeyer (ICLR, 2025)

MarS-FM: Generative Modeling of Molecular Dynamics via Markov State Models. - Kacper Kapusniak; Cristian Gabellini; Michael M. Bronstein; Prudencio Tossou; Francesco Di Giovanni (CoRR, 2025)