Simplicial Representation Learning with Neural $k$-Forms

1 code implementation • 13 Dec 2023 • Kelly Maggs, Celia Hacker, Bastian Rieck

In this paper, we take a different approach, focusing on leveraging geometric information from simplicial complexes embedded in $\mathbb{R}^n$ using node coordinates.

Representation Learning

On the Surprising Behaviour of node2vec

1 code implementation • 16 Jun 2022 • Celia Hacker, Bastian Rieck

Graph embedding techniques are a staple of modern graph learning research.

Graph Embedding Graph Learning

Capturing Graphs with Hypo-Elliptic Diffusions

1 code implementation • 27 May 2022 • Csaba Toth, Darrick Lee, Celia Hacker, Harald Oberhauser

This results in a novel tensor-valued graph operator, which we call the hypo-elliptic graph Laplacian.

k-simplex2vec: a simplicial extension of node2vec

1 code implementation • 12 Oct 2020 • Celia Hacker

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.

BIG-bench Machine Learning

$k$-simplex2vec: a simplicial extension of node2vec

no code implementations • NeurIPS Workshop TDA_and_Beyond 2020 • Celia Hacker

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.

BIG-bench Machine Learning