Francesco Di Giovanni | DeepAI

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∙ 06/06/2023

How does over-squashing affect the power of GNNs?

Graph Neural Networks (GNNs) are the state-of-the-art model for machine ...

0 Francesco Di Giovanni, et al. ∙

∙ 05/17/2023

Edge Directionality Improves Learning on Heterophilic Graphs

Graph Neural Networks (GNNs) have become the de-facto standard tool for ...

0 Emanuele Rossi, et al. ∙

∙ 05/13/2023

DRew: Dynamically Rewired Message Passing with Delay

Message passing neural networks (MPNNs) have been shown to suffer from t...

0 Benjamin Gutteridge, et al. ∙

∙ 02/06/2023

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

Message Passing Neural Networks (MPNNs) are instances of Graph Neural Ne...

0 Francesco Di Giovanni, et al. ∙

∙ 06/22/2022

Graph Neural Networks as Gradient Flows

Dynamical systems minimizing an energy are ubiquitous in geometry and ph...

101 Francesco Di Giovanni, et al. ∙

∙ 02/09/2022

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

Cellular sheaves equip graphs with "geometrical" structure by assigning ...

120 Cristian Bodnar, et al. ∙

∙ 02/02/2022

Heterogeneous manifolds for curvature-aware graph embedding

Graph embeddings, wherein the nodes of the graph are represented by poin...

13 Francesco Di Giovanni, et al. ∙

∙ 11/29/2021

Understanding over-squashing and bottlenecks on graphs via curvature

Most graph neural networks (GNNs) use the message passing paradigm, in w...

1 Jake Topping, et al. ∙

∙ 10/18/2021

Beltrami Flow and Neural Diffusion on Graphs

We propose a novel class of graph neural networks based on the discretis...

74 Benjamin Paul Chamberlain, et al. ∙

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