The visualization of dynamic graphs is a challenging task owing to the various properties of the underlying relational data and the additional time-varying property. For sparse and small graphs, the most efficient approach to such visualization is node-link diagrams, whereas for dense graphs with attached data, adjacency matrices might be the better choice. Because graphs can contain both properties, being globally sparse and locally dense, a combination of several visual metaphors as well as static and dynamic visualizations is beneficial. In this paper, a visually and algorithmically scalable approach that provides views and perspectives on graphs as interactively linked node-link and adjacency matrix visualizations is described. As the novelty of this technique, insights such as clusters or anomalies from one or several combined views can be used to influence the layout or reordering of the other views. Moreover, the importance of nodes and node groups can be detected, computed, and visualized by considering several layout and reordering properties in combination as well as different edge properties for the same set of nodes. As an additional feature set, an automatic identification of groups, clusters, and outliers is provided over time, and based on the visual outcome of the node-link and matrix visualizations, the repertoire of the supported layout and matrix reordering techniques is extended, and more interaction techniques are provided when considering the dynamics of the graph data. Finally, a small user experiment was conducted to investigate the usability of the proposed approach. The usefulness of the proposed tool is illustrated by applying it to a graph dataset, such as e co-authorships, co-citations, and a Comprehensible Perl Archive Network distribution.

中文翻译：

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通过交互式链接节点链接图和矩阵可视化进行动态图探索

由于底层关系数据的各种特性和附加的时变特性，动态图的可视化是一项具有挑战性的任务。对于稀疏和小图，这种可视化最有效的方法是节点链接图，而对于带有附加数据的密集图，邻接矩阵可能是更好的选择。由于图可以同时包含全局稀疏和局部密集这两种属性，因此结合使用多个视觉隐喻以及静态和动态可视化是有益的。在本文中，描述了一种视觉上和算法上可扩展的方法，该方法以交互式链接的节点链接和邻接矩阵可视化形式提供图形的视图和视角。由于这项技术的新颖性，来自一个或多个组合视图的集群或异常等洞察力可用于影响其他视图的布局或重新排序。此外，通过结合考虑多个布局和重新排序属性以及同一组节点的不同边属性，可以检测、计算和可视化节点和节点组的重要性。作为附加功能集，随着时间的推移提供组、集群和异常值的自动识别，并基于节点链接和矩阵可视化的视觉结果，扩展了支持的布局和矩阵重新排序技术的全部内容，并且在考虑图形数据的动态时，提供了更多的交互技术。最后，进行了一个小型用户实验来研究所提出方法的可用性。