# Towards a Survey on Static and Dynamic Hypergraph Visualizations

arXiv: https://arxiv.org/abs/2107.13936 · HTML: https://mlsanity.com/paper/2107.13936/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jul 2021.

## Built on by (most cited first, 5 of 5)

- [Architectures of Topological Deep Learning: A Survey of Message-Passing Topological Neural Networks](https://mlsanity.com/paper/2304.10031/) (2023)
- [VAC2: Visual Analysis of Combined Causality in Event Sequences](https://mlsanity.com/paper/2206.05420/) (2022)
- [Multi-Excitation Projective Simulation with a Many-Body Physics Inspired Inductive Bias](https://mlsanity.com/paper/2402.10192/) (2024)
- [HINTs: Sensemaking on large collections of documents with Hypergraph visualization and INTelligent agents](https://mlsanity.com/paper/2403.02752/) (2024)
- [DGSVis: Visual Analysis of Hierarchical Snapshots in Dynamic Graph](https://mlsanity.com/paper/2205.13220/) (2022)

## Abstract (arXiv)

Leveraging hypergraph structures to model advanced processes has gained much attention over the last few years in many areas, ranging from protein-interaction in computational biology to image retrieval using machine learning. Hypergraph models can provide a more accurate representation of the underlying processes while reducing the overall number of links compared to regular representations. However, interactive visualization methods for hypergraphs and hypergraph-based models have rarely been explored or systematically analyzed. This paper reviews the existing research landscape for hypergraph and hypergraph model visualizations and assesses the currently employed techniques. We provide an overview and a categorization of proposed approaches, focusing on performance, scalability, interaction support, successful evaluation, and the ability to represent different underlying data structures, including a recent demand for a temporal representation of interaction networks and their improvements beyond graph-based methods. Lastly, we discuss the strengths and weaknesses of the approaches and give an insight into the future challenges arising in this emerging research field.