mlsanityData to 7 Oct 2026

Can an NN model plainly learn planar layouts?

arXiv 2209.01075 ↗First posted 2 Sep 2022cs.CG cs.DS cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 23 references that are in the map. First posted on arXiv 2 Sep 2022.

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6 of 23 references.

References · 6

  1. 2019A Deep Generative Model for Graph Layoutcited by 19
  2. 2019DeepDrawing: A Deep Learning Approach to Graph Drawingcited by 15
  3. 2018Evaluating the Readability of Force Directed Graph Layouts: A Deep Learning Approachcited by 14
  4. 2019Toward automatic comparison of visualization techniques: Application to graph visualizationcited by 8
  5. 2021Deep Neural Network for DrawiNg Networks, (DNN)^2cited by 6
  6. 2018Aesthetic Discrimination of Graph Layoutscited by 5

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