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.

翻译：平面图画往往具有美感。 在这幅海报中,我们探索了神经网络学习各种平面图类的能力。 此外,我们还调查了模型在全面推广超出平面图类方面的有效性。我们发现模型在某些图形类中可以超过常规技术。然而,模型似乎更容易受到数据随机性的影响,而且似乎比预期的要弱。