图形布局的美学辨析
Aesthetic Discrimination of Graph Layouts
摘要：
本文解决了以下基本问题：给定两个布局相同的图，哪一个是更美观？我们提出了一个基于神经网络的布局模型，该模型在一个标记的数据集上训练，该数据集决定两个布局中哪一个具有更高的美学质量。用作模型输入的特征向量基于已知的图形绘制质量度量、经典统计、信息理论量和受凝聚态物理方法启发的两点统计。用于训练和测试的布局对的大型语料库是使用力导向绘图算法和自然源于图形生成过程的布局来构建的。它使用数据增强技术进一步扩展。我们的模型的平均预测准确率为95.70%，优于基于压力和流行质量指标的线性组合的判别器。
英文摘要：
This paper addresses the following basic question: given two layouts of the same graph, which one is more aesthetically pleasing? We propose a neural network-based discriminator model trained on a labeled dataset that decides which of two layouts has a higher aesthetic quality. The feature vectors used as inputs to the model are based on known graph drawing quality metrics, classical statistics, information-theoretical quantities, and two-point statistics inspired by methods of condensed matter physics. The large corpus of layout pairs used for training and testing is constructed using force-directed drawing algorithms and the layouts that naturally stem from the process of graph generation. It is further extended using data augmentation techniques. The mean prediction accuracy of our model is 95.70%, outperforming discriminators based on stress and on the linear combination of popular quality metrics by a statistically significant margin.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
