GkGNN extends GNN message passing from neighborhoods to covers via category theory, with the Sieve Neural Networks instantiation achieving zero failures on SRG, CSL, and BREC isomorphism benchmarks.
A survey on the expressive power of graph neural networks.arXiv:2003.04078
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
Universal spin models are universal approximators of probability distributions, yielding a unified recipe for universal approximation theorems in models such as restricted Boltzmann machines and deep belief networks.
S³GNN mitigates oversquashing in message-passing networks via lightweight global mixing without strong prior assumptions, yielding up to 10x error reduction and 50% fewer parameters across multiple domains.
citing papers explorer
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Grothendieck Graph Neural Networks Framework: An Algebraic Platform for Crafting Topology-Aware GNNs
GkGNN extends GNN message passing from neighborhoods to covers via category theory, with the Sieve Neural Networks instantiation achieving zero failures on SRG, CSL, and BREC isomorphism benchmarks.
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Universal Spin Models are Universal Approximators in Machine Learning
Universal spin models are universal approximators of probability distributions, yielding a unified recipe for universal approximation theorems in models such as restricted Boltzmann machines and deep belief networks.
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S$^3$GNN: Efficient Global Mixing and Local Message Passing for Long-Range Graph Learning
S³GNN mitigates oversquashing in message-passing networks via lightweight global mixing without strong prior assumptions, yielding up to 10x error reduction and 50% fewer parameters across multiple domains.