arXiv Machine Learning June 5, 2026 By Guillermo Bern\'ardez, Marco Montagna, Louis Van Langendonck, Martin Carrasco, Amirreza Akbari, Louisa Cornelis, Mathilde Papillon, Pere Barlet-Ros, Nina Miolane, Lev Telyatnikov

HOPSE: Scalable Higher-Order Positional and Structural Encoder for Combinatorial Representations

Read the original on arXiv Machine Learning →

arXiv:2505. 15405v3 Announce Type: replace Abstract: While Graph Neural Networks (GNNs) have proven highly effective at modeling relational data, pairwise connections cannot fully capture multi-way relationships naturally present in complex real-world systems.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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arXiv Machine Learning

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Differentiable Lifting for Topological Neural Networks

arXiv:2608. 01160v1 Announce Type: new Abstract: Topological neural networks (TNNs) enable leveraging high-order structures on graphs (e.

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arXiv Machine Learning

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llms rag benchmarks

arXiv Machine Learning

Jun 17

Weisfeiler Lehman Test on Combinatorial Complexes: Generalized Expressive Power of Topological Neural Networks

arXiv:2605. 00725v2 Announce Type: replace Abstract: Topological neural networks have emerged as effective tools for modeling higher-order relational structures beyond pairwise graphs, including hypergraphs, simplicial complexes, and cell complexes.

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arXiv Machine Learning

1d ago

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By Mathilde Papillon, Guillermo Bern\'ardez, \'Alvaro Ball\'on Barreiro, Marco Montagna, R\'emi Devaux, Antoine Jardin, Nina Miolane

arXiv Machine Learning

Aug 11

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By Isuru Herath, Arin Gopakumar, Sharan Sahu

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arXiv AI

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Plain Transformers are Surprisingly Powerful Link Predictors

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By Quang Truong, Yu Song, Donald Loveland, Mingxuan Ju, Tong Zhao, Neil Shah, Jiliang Tang

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Differentiable Lifting for Topological Neural Networks

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