$k$-Simplex2Vec: A Simplicial Extension of Node2Vec

Abstract

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.

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Cite

Text

Hacker. "$k$-Simplex2Vec: A Simplicial Extension of Node2Vec." NeurIPS 2020 Workshops: TDA_and_Beyond, 2020.

Markdown

[Hacker. "$k$-Simplex2Vec: A Simplicial Extension of Node2Vec." NeurIPS 2020 Workshops: TDA_and_Beyond, 2020.](https://mlanthology.org/neuripsw/2020/hacker2020neuripsw-ksimplex2vec/)

BibTeX

@inproceedings{hacker2020neuripsw-ksimplex2vec,
  title     = {{$k$-Simplex2Vec: A Simplicial Extension of Node2Vec}},
  author    = {Hacker, Celia},
  booktitle = {NeurIPS 2020 Workshops: TDA_and_Beyond},
  year      = {2020},
  url       = {https://mlanthology.org/neuripsw/2020/hacker2020neuripsw-ksimplex2vec/}
}