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How does over-squashing affect the power of GNNs?
In this paper, we provide a rigorous analysis to determine which function classes of node features can be learned by an MPNN of a given capacity.
Expander Graph Propagation
Deploying graph neural networks (GNNs) on whole-graph classification or regression tasks is known to be challenging: it often requires computing node features that are mindful of both local interactions in their neighbourhood and the global context of the graph structure.
The signature and cusp geometry of hyperbolic knots
We introduce a new real-valued invariant called the natural slope of a hyperbolic knot in the 3-sphere, which is defined in terms of its cusp geometry.
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