# Hypergraph Echo State Network

arXiv: https://arxiv.org/abs/2310.10177 · HTML: https://mlsanity.com/paper/2310.10177/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 30 references that are in the map.
- First posted on arXiv 16 Oct 2023.

## Builds on (most cited first, 5 of 5)

- [Hypergraph Neural Networks](https://mlsanity.com/paper/1809.09401/) (2018)
- [Graph neural networks for materials science and chemistry](https://mlsanity.com/paper/2208.09481/) (2022)
- [Analysis and Control of Epidemics: A survey of spreading processes on complex networks](https://mlsanity.com/paper/1505.00768/) (2015)
- [Line Hypergraph Convolution Network: Applying Graph Convolution for Hypergraphs](https://mlsanity.com/paper/2002.03392/) (2020)
- [Epidemics on Hypergraphs: Spectral Thresholds for Extinction](https://mlsanity.com/paper/2103.07319/) (2021)

## Abstract (arXiv)

A hypergraph as a generalization of graphs records higher-order interactions among nodes, yields a more flexible network model, and allows non-linear features for a group of nodes. In this article, we propose a hypergraph echo state network (HypergraphESN) as a generalization of graph echo state network (GraphESN) designed for efficient processing of hypergraph-structured data, derive convergence conditions for the algorithm, and discuss its versatility in comparison to GraphESN. The numerical experiments on the binary classification tasks demonstrate that HypergraphESN exhibits comparable or superior accuracy performance to GraphESN for hypergraph-structured data, and accuracy increases if more higher-order interactions in a network are identified.