Existing dynamic weighted graph visualization approaches rely on users' mental comparison to perceive temporal evolution of dynamic weighted graphs, hindering users from effectively analyzing changes across multiple timeslices. We propose DiffSeer, a novel approach for dynamic weighted graph visualization by explicitly visualizing the differences of graph structures (e.g., edge weight differences) between adjacent timeslices. Specifically, we present a novel nested matrix design that overviews the graph structure differences over a time period as well as shows graph structure details in the timeslices of user interest. By collectively considering the overall temporal evolution and structure details in each timeslice, an optimization-based node reordering strategy is developed to group nodes with similar evolution patterns and highlight interesting graph structure details in each timeslice. We conducted two case studies on real-world graph datasets and in-depth interviews with 12 target users to evaluate DiffSeer. The results demonstrate its effectiveness in visualizing dynamic weighted graphs.

翻译：现有动态加权图可视化方法依赖用户通过心理比较来感知动态加权图的时间演变，这阻碍了用户有效分析多个时间切片间的变化。我们提出DiffSeer，一种通过显式可视化相邻时间切片间图结构差异（如边权重差异）的动态加权图可视化新方法。具体而言，我们提出一种新颖的嵌套矩阵设计，既能概览一段时间内的图结构差异，又能展示用户感兴趣时间切片中的图结构细节。通过综合考量整体时间演变与各时间切片的结构细节，我们开发了一种基于优化的节点重排策略，用于聚集具有相似演变模式的节点并突出各时间切片中感兴趣的图结构细节。我们基于真实图数据集开展两项案例研究，并对12名目标用户进行深度访谈以评估DiffSeer。结果表明该方法在可视化动态加权图方面具有有效性。