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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 ...
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05/17/2023
Edge Directionality Improves Learning on Heterophilic Graphs
Graph Neural Networks (GNNs) have become the de-facto standard tool for ...
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05/13/2023
DRew: Dynamically Rewired Message Passing with Delay
Message passing neural networks (MPNNs) have been shown to suffer from t...
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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...
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06/22/2022
Graph Neural Networks as Gradient Flows
Dynamical systems minimizing an energy are ubiquitous in geometry and ph...
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02/09/2022
Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs
Cellular sheaves equip graphs with "geometrical" structure by assigning ...
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02/02/2022
Heterogeneous manifolds for curvature-aware graph embedding
Graph embeddings, wherein the nodes of the graph are represented by poin...
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11/29/2021
Understanding over-squashing and bottlenecks on graphs via curvature
Most graph neural networks (GNNs) use the message passing paradigm, in w...
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10/18/2021
