Dynamic Network Visualization withExtended Massive Sequence Views
Dynamic Network Visualization withExtended Massive Sequence Views
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DOI：
10.1109/tvcg.2013.263
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发表时间：
2014-08
影响因子：
5.2
通讯作者：
S. V. D. Elzen;Danny Holten;Jorik Blaas;J. V. Wijk
中科院分区：
文献类型：
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作者：
S. V. D. Elzen;Danny Holten;Jorik Blaas;J. V. Wijk
网络存在于许多领域，如金融、社会学和交通运输。这些网络通常是动态的：它们既有结构方面的，也有时间方面的。除了随时间发生的关系之外，还经常出现节点信息，例如分层结构或时间序列数据。我们提出了一种扩展海量序列视图(MSV)的技术，用于动态网络的时间和结构方面的分析。利用数据中的特征以及可视化中的格式塔原理，如闭包、邻近和相似，我们为MSV开发了节点重排序策略，以使这些特征突出，并可选地考虑到层次节点结构。这使用户能够找到网络中的时间属性，如趋势、逆趋势、周期、时间变化和异常，以及结构属性，如社区和恒星。我们引入了圆形MSV，进一步减少了视觉杂波。此外，(循环)MSV也被扩展以传送与节点相关联的时间序列数据。这使用户能够分析边出现和节点属性变化之间的复杂关联。我们展示了重排序方法在合成数据集和丰富的真实动态网络数据集上的有效性。
Networks are present in many fields such as finance, sociology, and transportation. Often these networks are dynamic: they have a structural as well as a temporal aspect. In addition to relations occurring over time, node information is frequently present such as hierarchical structure or time-series data. We present a technique that extends the Massive Sequence View ( msv) for the analysis of temporal and structural aspects of dynamic networks. Using features in the data as well as Gestalt principles in the visualization such as closure, proximity, and similarity, we developed node reordering strategies for the msv to make these features stand out that optionally take the hierarchical node structure into account. This enables users to find temporal properties such as trends, counter trends, periodicity, temporal shifts, and anomalies in the network as well as structural properties such as communities and stars. We introduce the circular msv that further reduces visual clutter. In addition, the (circular) msv is extended to also convey time-series data associated with the nodes. This enables users to analyze complex correlations between edge occurrence and node attribute changes. We show the effectiveness of the reordering methods on both synthetic and a rich real-world dynamic network data set.