Connectivity analysis between the components of large evolving systems can reveal significant patterns of interaction. The systems can be simulated by topological graph structures. However, such analysis becomes challenging on large and complex graphs. Tasks such as comparing, searching, and summarizing structures, are difficult due to the enormous number of calculations required. For time-varying graphs, the temporal dimension even intensifies the difficulty. In this article, we propose to reduce the complexity of analysis by focusing on subgraphs that are induced by closely related entities. To summarize the diverse structures of subgraphs, we build a supervised layout-based classification model. The main premise is that the graph structures can induce a unique appearance of the layout. In contrast to traditional graph theory-based and contemporary neural network-based methods of graph classification, our approach generates low costs and there is no need to learn informative graph representations. Combined with temporally stable visualizations, we can also facilitate the understanding of sub-structures and the tracking of graph evolution. The method is evaluated on two real-world datasets. The results show that our system is highly effective in carrying out visual-based analytics of large graphs.

中文翻译：

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一种基于布局的时变图可视化分类方法

大型演化系统的组件之间的连通性分析可以揭示重要的交互模式。这些系统可以通过拓扑图结构来模拟。然而，这样的分析在大而复杂的图上变得具有挑战性。由于需要大量计算，比较、搜索和总结结构等任务很困难。对于时变图，时间维度甚至加剧了难度。在本文中，我们建议通过关注由密切相关实体诱导的子图来降低分析的复杂性。为了总结子图的不同结构，我们构建了一个基于监督布局的分类模型。主要前提是图形结构可以引起布局的独特外观。与传统的基于图论和当代基于神经网络的图分类方法相比，我们的方法产生低成本并且不需要学习信息丰富的图表示。结合时间稳定的可视化，我们还可以促进对子结构的理解和图演化的跟踪。该方法在两个真实世界的数据集上进行评估。结果表明，我们的系统在对大图进行基于视觉的分析方面非常有效。该方法在两个真实世界的数据集上进行评估。结果表明，我们的系统在对大图进行基于视觉的分析方面非常有效。该方法在两个真实世界的数据集上进行评估。结果表明，我们的系统在对大图进行基于视觉的分析方面非常有效。