A dynamic network refers to a graph structure whose nodes and/or links dynamically change over time. Existing visualization and analysis techniques focus mainly on summarizing and revealing the primary evolution patterns of the network structure. Little work focuses on detecting anomalous changing patterns in the dynamic network, the rare occurrence of which could damage the development of the entire structure. In this study, we introduce the first visual analysis system RCAnalyzer designed for detecting rare changes of sub-structures in a dynamic network. The proposed system employs a rare category detection algorithm to identify anomalous changing structures and visualize them in the context to help oracles examine the analysis results and label the data. In particular, a novel visualization is introduced, which represents the snapshots of a dynamic network in a series of connected triangular matrices. Hierarchical clustering and optimal tree cut are performed on each matrix to illustrate the detected rare change of nodes and links in the context of their surrounding structures. We evaluate our technique via a case study and a user study. The evaluation results verify the effectiveness of our system.

动态网络是指其节点和/或链接随时间动态变化的图结构。现有的可视化和分析技术主要集中在总结和揭示网络结构的主要演化模式。很少有工作专注于检测动态网络中的异常变化模式，这种情况很少发生，可能会破坏整个结构的发展。在这项研究中，我们介绍了第一个视觉分析系统RCAnalyzer，其设计用于检测动态网络中子结构的罕见变化。提出的系统采用稀有类别检测算法来识别异常变化的结构，并在上下文中将其可视化，以帮助Oracle检查分析结果并标记数据。特别是介绍了一种新颖的可视化效果，它表示一系列连接的三角矩阵中动态网络的快照。在每个矩阵上进行层次聚类和最优树切割，以说明在其周围结构的上下文中检测到的节点和链接的罕见变化。我们通过案例研究和用户研究来评估我们的技术。评估结果证明了我们系统的有效性。