Visual Analytics for Temporal Hypergraph Model Exploration
Maximilian T. Fischer, Devanshu Arya, Dirk Streeb, Daniel Seebacher,, Daniel A. Keim, Marcel Worring

TL;DR
This paper introduces Hyper-Matrix, a visual analytics tool that combines machine learning and interactive visualization to explore and refine temporal hypergraph models, enhancing scalability and domain-specific analysis.
Contribution
The paper presents Hyper-Matrix, a novel visual analytics approach integrating geometric deep learning with interactive visualizations for temporal hypergraph exploration.
Findings
Outperforms existing solutions in scalability and applicability
Enables incorporation of domain knowledge
Facilitates fast search-space traversal
Abstract
Many processes, from gene interaction in biology to computer networks to social media, can be modeled more precisely as temporal hypergraphs than by regular graphs. This is because hypergraphs generalize graphs by extending edges to connect any number of vertices, allowing complex relationships to be described more accurately and predict their behavior over time. However, the interactive exploration and seamless refinement of such hypergraph-based prediction models still pose a major challenge. We contribute Hyper-Matrix, a novel visual analytics technique that addresses this challenge through a tight coupling between machine-learning and interactive visualizations. In particular, the technique incorporates a geometric deep learning model as a blueprint for problem-specific models while integrating visualizations for graph-based and category-based data with a novel combination of…
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