Work

HyperGodot: Interactive hypergraph visualization tool

Year: 2025

Type: software-paper

Abstract: Hypergraphs provide a robust framework for modeling complex, multi-actor interactions that traditional graphs struggle to represent. In many real-world applications, interactions involve more than just pairs of entities, which makes hypergraphs an essential tool for capturing these multidimensional relationships. Although there are various visualization tools, they often face challenges when handling large- or dense-scale hypergraphs, where overlapping vertices and edges impede clarity and interpretation. The proposed visualization tool addresses these challenges by providing an interactive and feature-rich environment to explore hypergraphs. The tool has been built using the Godot Engine, exploiting the potential its efficient rendering, real-time interactivity, and adaptable scripting to facilitate smooth and intuitive hypergraph visualization. Users can import or manually create hypergraphs, choose from multiple layouts (force-directed, circular, grid), and customize hyperedge widths and vertex sizes for clearer insights. Edge colors are adjustable for enhanced distinction. Hyperedges are rendered using Andrew’s monotone chain algorithm to avoid overlap even in large-scale hypergraphs. In addition to these features, users can define multiple groups within the hypergraph, where each group has a distinct visual style, allowing for direct comparison of two or more hypergraphs by merging them into one unified view. This facilitates comparative analysis and makes it easier to observe differences and similarities between various hypergraph structures. Moreover, users can interactively explore the visualization by selecting vertices to isolate edges and using a smooth camera system for zooming and navigation, enabling both macro- and micro-level analysis. We believe that this niche interactive hypergraph visualization tool can enhance research activities and facilitate deeper analytical insights.

Source: SoftwareX

DOI: 10.1016/j.softx.2025.102315

Authors: Attila Ficsor, Timea Czvetko, Viktor Sebestyen, Janos Abonyi

Institution: University of Pannonia

Language: English

Topic: Data Visualization and Analytics

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,200

Funders: Hungarian Scientific Research Fund, Nemzeti Kutatási, Fejlesztési és Innovaciós Alap