Large tree structures are ubiquitous and real-world relational datasets often have information associated with nodes (e.g., labels or other attributes) and edges (e.g., weights or distances) that need to be communicated to the viewers. Yet, scalable, easy to read tree layouts are difficult to achieve. We consider tree layouts to be readable if they meet some basic requirements: node labels should not overlap, edges should not cross, edge lengths should be preserved, and the output should be compact. There are many algorithms for drawing trees, although very few take node labels or edge lengths into account, and none optimizes all requirements above. With this in mind, we propose a new scalable method for readable tree layouts. The algorithm guarantees that the layout has no edge crossings and no label overlaps, and optimizes one of the remaining aspects: desired edge lengths and compactness. We evaluate the performance of the new algorithm by comparison with related earlier approaches using several real-world datasets, ranging from a few thousand nodes to hundreds of thousands of nodes. Tree layout algorithms can be used to visualize large general graphs, by extracting a hierarchy of progressively larger trees. We illustrate this functionality by presenting several map-like visualizations generated by the new tree layout algorithm.

翻译：大规模树状结构普遍存在，现实世界的关系数据集通常具有与节点（例如标签或其他属性）和边（例如权重或距离）相关的信息，这些信息需要向观察者传达。然而，实现可扩展且易于阅读的树布局十分困难。我们认为，如果树布局满足以下基本要求，则是可读的：节点标签不应重叠，边不应交叉，边长度应保持不变，并且输出应紧凑。尽管有许多绘制树的算法，但很少有算法考虑节点标签或边长度，并且没有算法能同时优化上述所有要求。基于此，我们提出了一种用于可读树布局的新可扩展方法。该算法保证布局中无边交叉和标签重叠，并优化了剩余方面之一：期望的边长度和紧凑性。通过与早期相关方法的比较，我们使用多个真实数据集（包含几千到几十万个节点）评估了新算法的性能。树布局算法可通过提取渐进式增大的树的层级结构来可视化大型常规图。我们通过展示由新树布局算法生成的几种类地图可视化，说明了这一功能。