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.

翻译：尽管许多图绘制算法将节点视为点，但图可视化工具常将其表示为形状。这些形状支持显示标签等信息，或通过尺寸与颜色编码多种数据。然而，它们可能在节点间产生重叠，从而通过隐藏部分信息阻碍探索过程。因此，为提升图可视化可读性，消除这些重叠至关重要。若布局过程未处理重叠问题，已提出重叠消除算法作为布局后处理手段。由于图布局通常传递拓扑信息，重叠消除算法需尽可能保持原始布局结构。本文提出一种新颖算法，将重叠消除建模为联合应力与缩放优化问题，并利用高效随机梯度下降求解。该方法与现有最优算法进行对比，多项质量指标证明其能在快速消除重叠的同时有效保持初始布局结构。