Order diagrams allow human analysts to understand and analyze structural properties of ordered data. While an experienced expert can create easily readable order diagrams, the automatic generation of those remains a hard task. In this work, we adapt force-directed approaches, which are known to generate aesthetically-pleasing drawings of graphs, to the realm of order diagrams. Our algorithm ReDraw thereby embeds the order in a high dimension and then iteratively reduces the dimension until a two-dimensional drawing is achieved. To improve aesthetics, this reduction is equipped with two force-directed steps where one optimizes on distances of nodes and the other on distances of lines in order to satisfy a set of a priori fixed conditions. By respecting an invariant about the vertical position of the elements in each step of our algorithm we ensure that the resulting drawings satisfy all necessary properties of order diagrams. Finally, we present the results of a user study to demonstrate that our algorithm outperforms comparable approaches on drawings of lattices with a high degree of distributivity.
翻译:命令图使人类分析师能够理解和分析定购数据的结构属性。 虽然有经验的专家可以创建容易读取的定序图, 但自动生成这些图仍然是一项艰巨的任务。 在这项工作中, 我们将已知生成图表的审美性图画的强制定向方法适应于顺序图的范围。 我们的算法“ 重新绘制” 将顺序嵌入高维度, 然后迭接地缩小维度, 直至实现双维绘图。 为改善审美, 这种减法配有两种由力量引导的步骤, 即优化节点的距离和线线的距离, 以满足一套先行固定条件。 通过尊重关于我们算法中每步元素垂直位置的变量, 我们确保由此产生的绘图能够满足秩序图的所有必要属性。 最后, 我们展示了用户研究结果, 以证明我们的算法在绘制具有高度分解性的拉特克时, 的算法比得相近。