Euler diagrams drawn with ellipses area-proportionally (Edeap)

Area of Science:

• Data Visualization
• Computational Biology
• Network Analysis

Background:

• Area-proportional Euler diagrams are vital for visualizing complex datasets in fields like microarray analysis and biosciences.
• These diagrams are increasingly applied across diverse domains, including social networks and general data representation.

Purpose of the Study:

• To introduce Edeap, a novel, simple, and scalable method for generating area-proportional Euler diagrams utilizing ellipses.
• To provide a comprehensive evaluation of Edeap against existing state-of-the-art methods for Euler diagram generation.

Main Methods:

• Edeap employs a search-based technique to optimize a multi-criteria objective function for area accuracy and usability.
• The evaluation compares Edeap with circle-based (veneuler) and ellipse-based (eulerr) methods using real-world and synthetic datasets.

Main Results:

• In terms of accuracy, the ordering from best to worst is eulerr, Edeap, and venneuler.
• Edeap offers a balance between accuracy and runtime, outperforming eulerr in speed and venneuler in scalability.
• Edeap and venneuler can handle up to 20 sets, while eulerr is limited to 11 sets.

Conclusions:

• Edeap presents a scalable and effective solution for creating area-proportional Euler diagrams, particularly for larger datasets.
• The choice between Edeap, eulerr, and venneuler depends on specific project needs regarding accuracy, runtime, and scalability.
• Edeap is available as open-source software and a web tool, promoting accessibility for researchers.