Depending on the node ordering, an adjacency matrix can highlight distinct characteristics of a graph. Deriving a "proper" node ordering is thus a critical step in visualizing a graph as an adjacency matrix. Users often try multiple matrix reorderings using different methods until they find one that meets the analysis goal. However, this trial-and-error approach is laborious and disorganized, which is especially challenging for novices. This paper presents a technique that enables users to effortlessly find a matrix reordering they want. Specifically, we design a generative model that learns a latent space of diverse matrix reorderings of the given graph. We also construct an intuitive user interface from the learned latent space by creating a map of various matrix reorderings. We demonstrate our approach through quantitative and qualitative evaluations of the generated reorderings and learned latent spaces. The results show that our model is capable of learning a latent space of diverse matrix reorderings. Most existing research in this area generally focused on developing algorithms that can compute "better" matrix reorderings for particular circumstances. This paper introduces a fundamentally new approach to matrix visualization of a graph, where a machine learning model learns to generate diverse matrix reorderings of a graph.
翻译:根据节点顺序, 匹配矩阵可以突出图表的不同特性。 因此, 匹配“ 适当” 节点是将图表作为相邻矩阵来视觉化的关键步骤。 用户经常使用不同的方法尝试多个矩阵重新排序, 直到他们找到符合分析目标的方法。 但是, 这种试和试方法既困难又混乱, 对于新点来说特别具有挑战性。 本文展示了一种使用户能够不遗余力地找到他们想要的矩阵重新排序的技术。 具体地说, 我们设计了一种基因化模型, 以学习不同矩阵重新排序的隐性空间。 我们还从学到的潜在空间里建立一个直观的用户界面, 创建了各种矩阵重新排序的地图。 我们通过对生成的重新排序和学习潜在空间的定量和定性评估来展示我们的方法。 结果表明, 我们的模式能够学习不同矩阵重新排序的潜在空间。 这一领域现有的多数研究一般侧重于开发能够将“ 更精确地” 矩阵重新排序的模型重新排序, 从而为不同的图表制成一个基本的矩阵式, 学习一种不同的矩阵式矩阵式, 向不同的矩阵向不同的矩阵方向学习。