论文标题
禁止：通过随机梯度下降以快速重叠去除图形图
FORBID: Fast Overlap Removal By stochastic gradIent Descent for Graph Drawing
论文作者
论文摘要
尽管许多图形绘图算法将节点视为点，但图形可视化工具通常表示它们为形状。这些形状支持显示信息，例如标签或用尺寸或颜色编码各种数据。但是，它们可以通过隐藏信息的一部分来阻碍探索过程之间的节点之间的重叠。因此，删除这些重叠以提高图形可视化可读性至关重要。如果未通过布局过程处理，则建议将重叠算法（或）算法作为布局后处理。由于图通常传达了有关其拓扑的信息，因此重要的是，算法要尽可能地保留它们。我们提出了一种新颖的算法，该算法是建模或作为关节应力和缩放优化问题，并利用有效的随机梯度下降。将这种方法与最先进的算法进行了比较，并且几种质量指标证明了其效率快速消除重叠的同时保留初始布局结构。
While many graph drawing algorithms consider nodes as points, graph visualization tools often represent them as shapes. These shapes support the display of information such as labels or encode various data with size or color. However, they can create overlaps between nodes which hinder the exploration process by hiding parts of the information. It is therefore of utmost importance to remove these overlaps to improve graph visualization readability. If not handled by the layout process, Overlap Removal (OR) algorithms have been proposed as layout post-processing. As graph layouts usually convey information about their topology, it is important that OR algorithms preserve them as much as possible. We propose a novel algorithm that models OR as a joint stress and scaling optimization problem, and leverages efficient stochastic gradient descent. This approach is compared with state-of-the-art algorithms, and several quality metrics demonstrate its efficiency to quickly remove overlaps while retaining the initial layout structures.
