mlsanityData to 14 Oct 2026

k-simplex2vec: a simplicial extension of node2vec

arXiv 2010.05636 ↗First posted 12 Oct 2020cs.LG math.ATcited by 13

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Abstract (arXiv)

We present a novel method of associating Euclidean features to simplicial complexes, providing a way to use them as input to statistical and machine learning tools. This method extends the node2vec algorithm to simplices of higher dimensions, providing insight into the structure of a simplicial complex, or into the higher-order interactions in a graph.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). First posted on arXiv 12 Oct 2020.

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Citing papers per year: 2021: 2; 2022: 2; 2023: 5; 2024: 3; 2025: 0; 2026: 1.

13 papers.

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  1. 2023Architectures of Topological Deep Learning: A Survey of Message-Passing Topological Neural Networkscited by 43
  2. 2024Beyond Euclid: An Illustrated Guide to Modern Machine Learning with Geometric, Topological, and Algebraic Structurescited by 20
  3. 2023Differentiable Euler Characteristic Transforms for Shape Classificationcited by 18
  4. 2023Simplicial Representation Learning with Neural k-Formscited by 18
  5. 2021Simplicial Complex Representation Learningcited by 17
  6. 2023Higher-order Graph Convolutional Network with Flower-Petals Laplacians on Simplicial Complexescited by 15
  7. 2023CAT-Walk: Inductive Hypergraph Learning via Set Walkscited by 6
  8. 2024Demystifying Higher-Order Graph Neural Networkscited by 4
  9. 2022On the Surprising Behaviour of node2veccited by 3
  10. 2021Accurately Modeling Biased Random Walks on Weighted Graphs Using textitNode2vec+cited by 2
  11. 2022Semantic-Native Communication: A Simplicial Complex Perspectivecited by 1
  12. 2024Learning From Simplicial Data Based on Random Walks and 1D Convolutionscited by 0
1 more
  1. 2026Modern Structure-Aware Simplicial Spatiotemporal Neural Networkcited by 0