We introduce an approach for the interactive visual analysis of weighted, dynamic networks. These networks arise in areas such as computational neuroscience, sociology, and biology. Network analysis remains challenging due to complex time-varying network behavior. For example, edges disappear/reappear, communities grow/vanish, or overall network topology changes. Our technique, TimeSum, detects the important topological changes in graph data to abstract the dynamic network and visualize one summary representation for each temporal phase, a state. We define a network state as a graph with similar topology over a specific time interval. To enable a holistic comparison of networks, we use a difference network to depict edge and community changes. We present case studies to demonstrate that our methods are effective and useful for extracting and exploring complex dynamic behavior of networks.

中文翻译：

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基于状态的网络相似度可视化

我们介绍了一种对加权动态网络进行交互式可视化分析的方法。这些网络出现在计算神经科学、社会学和生物学等领域。由于复杂的时变网络行为，网络分析仍然具有挑战性。例如，边缘消失/重新出现，社区增长/消失，或整体网络拓扑变化。我们的技术 TimeSum 检测图形数据中的重要拓扑变化，以抽象动态网络并可视化每个时间阶段（一种状态）的一个摘要表示。我们将网络状态定义为在特定时间间隔内具有相似拓扑的图。为了实现网络的整体比较，我们使用差异网络来描述边缘和社区的变化。