Year: 2022
Type: preprint
Abstract: Planar graph drawings tend to be aesthetically pleasing. In this poster we explore a Neural Network's capability of learning various planar graph classes. Additionally, we also investigate the effectiveness of the model in generalizing beyond planarity. We find that the model can outperform conventional techniques for certain graph classes. The model, however, appears to be more susceptible to randomness in the data, and seems to be less robust than expected.
Source: arXiv (Cornell University)
Authors: van Wageningen, Smon, Mchedlidze, Tamara
Institutions: University of Applied Sciences Utrecht, Utrecht University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4294786571