# Hypergraph Dissimilarity Measures

arXiv: https://arxiv.org/abs/2106.08206 · HTML: https://mlsanity.com/paper/2106.08206/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 58 references that are in the map.
- First posted on arXiv 15 Jun 2021.

## Builds on (most cited first, 17 of 17)

- [Hypergraph Neural Networks](https://mlsanity.com/paper/1809.09401/) (2018)
- [Hypergraph Convolution and Hypergraph Attention](https://mlsanity.com/paper/1901.08150/) (2019)
- [Graph Embedding Techniques, Applications, and Performance: A Survey](https://mlsanity.com/paper/1705.02801/) (2017)
- [A Survey on Graph Kernels](https://mlsanity.com/paper/1903.11835/) (2019)
- [Networks beyond pairwise interactions: structure and dynamics](https://mlsanity.com/paper/2006.01764/) (2020)
- [NetLSD: Hearing the Shape of a Graph](https://mlsanity.com/paper/1805.10712/) (2018)
- [Metrics for Graph Comparison: A Practitioner's Guide](https://mlsanity.com/paper/1904.07414/) (2019)
- [Layer aggregation and reducibility of multilayer interconnected networks](https://mlsanity.com/paper/1405.0425/) (2014)
- [Hypernetwork Science via High-Order Hypergraph Walks](https://mlsanity.com/paper/1906.11295/) (2019)
- [DELTACON: A Principled Massive-Graph Similarity Function](https://mlsanity.com/paper/1304.4657/) (2013)
- [Three hypergraph eigenvector centralities](https://mlsanity.com/paper/1807.09644/) (2018)
- [Classification in biological networks with hypergraphlet kernels](https://mlsanity.com/paper/1703.04823/) (2017)
- [Deep Hyperedges: a Framework for Transductive and Inductive Learning on Hypergraphs](https://mlsanity.com/paper/1910.02633/) (2019)
- [Higher-order Network Analysis Takes Off, Fueled by Classical Ideas and New Data](https://mlsanity.com/paper/2103.05031/) (2021)
- [Tensor Entropy for Uniform Hypergraphs](https://mlsanity.com/paper/1912.09624/) (2019)
- [A unifying Perron-Frobenius theorem for nonnegative tensors via multi-homogeneous maps](https://mlsanity.com/paper/1801.04215/) (2018)
- [Multilinear Time Invariant System Theory](https://mlsanity.com/paper/1905.07427/) (2019)

## Built on by (most cited first, 1 of 1)

- [Relational Learning in Pre-Trained Models: A Theory from Hypergraph Recovery Perspective](https://mlsanity.com/paper/2406.11249/) (2024)

## Abstract (arXiv)

In this paper, we propose two novel approaches for hypergraph comparison. The first approach transforms the hypergraph into a graph representation for use of standard graph dissimilarity measures. The second approach exploits the mathematics of tensors to intrinsically capture multi-way relations. For each approach, we present measures that assess hypergraph dissimilarity at a specific scale or provide a more holistic multi-scale comparison. We test these measures on synthetic hypergraphs and apply them to biological datasets.