Graphs are often used to model relationships between entities. The identification and visualization of clusters in graphs enable insight discovery in many application areas, such as life sciences and social sciences. Force-directed graph layouts promote the visual saliency of clusters, as they bring adjacent nodes closer together, and push non-adjacent nodes apart. At the same time, matrices can effectively show clusters when a suitable row/column ordering is applied, but are less appealing to untrained users not providing an intuitive node-link metaphor. It is thus worth exploring layouts combining the strengths of the node-link metaphor and node ordering. In this work, we study the impact of node ordering on the visual saliency of clusters in orderable node-link diagrams, namely radial diagrams, arc diagrams and symmetric arc diagrams. Through a crowdsourced controlled experiment, we show that users can count clusters consistently more accurately, and to a large extent faster, with orderable node-link diagrams than with three state-of-the art force-directed layout algorithms, i.e., `Linlog', `Backbone' and `sfdp'. The measured advantage is greater in case of low cluster separability and/or low compactness. A free copy of this paper and all supplemental materials are available at https://osf.io/kc3dg/.

翻译：图常用于建模实体间的关系。图中簇的识别与可视化有助于在生命科学、社会科学等众多应用领域发现洞察。力导向图布局通过使相邻节点更紧密、非相邻节点更远离，增强了簇的视觉显著性。同时，矩阵在采用合适的行/列排序时能有效展示簇结构，但对未受训练的用户而言缺乏直观的节点-链接隐喻。因此，探索结合节点-链接隐喻与节点排序优势的布局具有重要价值。本研究通过可控众包实验，系统评估了节点排序对可排序节点-链接图（径向图、弧线图及对称弧线图）中簇视觉显著性的影响。实验表明：相较于三种前沿力导向布局算法（`Linlog'、`Backbone' 与 `sfdp'），用户使用可排序节点-链接图时能更准确且更快速地识别簇数量。在簇分离度较低和/或紧凑性不足的情况下，这种优势更为显著。本文及相关补充材料可在 https://osf.io/kc3dg/ 免费获取。