# k-simplex2vec: a simplicial extension of node2vec

arXiv: https://arxiv.org/abs/2010.05636 · HTML: https://mlsanity.com/paper/2010.05636/

## Key facts

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

## Built on by (most cited first, 13 of 13)

- [Architectures of Topological Deep Learning: A Survey of Message-Passing Topological Neural Networks](https://mlsanity.com/paper/2304.10031/) (2023)
- [Beyond Euclid: An Illustrated Guide to Modern Machine Learning with Geometric, Topological, and Algebraic Structures](https://mlsanity.com/paper/2407.09468/) (2024)
- [Differentiable Euler Characteristic Transforms for Shape Classification](https://mlsanity.com/paper/2310.07630/) (2023)
- [Simplicial Representation Learning with Neural k-Forms](https://mlsanity.com/paper/2312.08515/) (2023)
- [Simplicial Complex Representation Learning](https://mlsanity.com/paper/2103.04046/) (2021)
- [Higher-order Graph Convolutional Network with Flower-Petals Laplacians on Simplicial Complexes](https://mlsanity.com/paper/2309.12971/) (2023)
- [CAT-Walk: Inductive Hypergraph Learning via Set Walks](https://mlsanity.com/paper/2306.11147/) (2023)
- [Demystifying Higher-Order Graph Neural Networks](https://mlsanity.com/paper/2406.12841/) (2024)
- [On the Surprising Behaviour of node2vec](https://mlsanity.com/paper/2206.08252/) (2022)
- [Accurately Modeling Biased Random Walks on Weighted Graphs Using textitNode2vec+](https://mlsanity.com/paper/2109.08031/) (2021)
- [Semantic-Native Communication: A Simplicial Complex Perspective](https://mlsanity.com/paper/2210.16970/) (2022)
- [Learning From Simplicial Data Based on Random Walks and 1D Convolutions](https://mlsanity.com/paper/2404.03434/) (2024)
- [Modern Structure-Aware Simplicial Spatiotemporal Neural Network](https://mlsanity.com/paper/2604.15833/) (2026)

## 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.