Interactive design and visualization of N-ary relationships

Botong Qu, Prashant Kumar, E. Zhang, P. Jaiswal, L. Cooper, J. Elser, Yue Zhang
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引用次数: 4

Abstract

Graph and network visualization is a well-researched area. However, graphs are limited in that by definition they are designed to encode pairwise relationships between the nodes in the graph. In this paper, we strive for visualization of datasets that contain not only binary relationships between the nodes, but also higher-cardinality relationships (ternary, quaternary, quinary, senary, etc). While such higher-cardinality relationships can be treated as cliques (a complete graph of N nodes), visualization of cliques using graph visualization can lead to unnecessary visual cluttering due to all the pairwise edges inside each clique. In this paper, we develop a visualization for data that have relationships with cardinalities higher than two. By representing each N-ary relationship as an N-sided polygon, we turn the problem of visualizing such data sets into that of visualizing a two-dimensional complex, i.e. nodes, edges, and polygonal faces. This greatly reduces the number of edges needed to represent a clique and makes them as well as their cardinalities more easily recognized. We develop a set of principles that measures the effectiveness of the visualization for two-dimensional complexes. Furthermore, we formulate our strategy with which the positions of the nodes in the complex and the orderings of the nodes inside each clique in the complex can be optimized. Furthermore, we allow the user to further improve the layout by moving a node or a polygon in 3D as well as changing the order of the nodes in a polygon. To demonstrate the effectiveness of our technique and system, we apply them to a social network and a gene dataset.
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n元关系的交互设计与可视化
图和网络可视化是一个研究得很好的领域。然而,图的局限性在于,根据定义,它们被设计为对图中节点之间的成对关系进行编码。在本文中,我们努力实现数据集的可视化,这些数据集不仅包含节点之间的二进制关系,还包含更高基数的关系(三元、四元、五元、四元等)。虽然这种高基数关系可以被视为团块(N个节点的完整图),但使用图形可视化来可视化团块可能会导致不必要的视觉混乱,因为每个团块内部都有成对的边。在本文中,我们开发了与基数大于2的关系的数据的可视化。通过将每个n元关系表示为n边多边形,我们将可视化这些数据集的问题转化为可视化二维复合体的问题,即节点、边和多边形面。这大大减少了表示团所需的边的数量,并使它们及其基数更容易识别。我们开发了一套测量二维复合物可视化效果的原则。在此基础上,提出了优化复合体中节点位置和各团内节点排序的策略。此外,我们允许用户通过在3D中移动节点或多边形以及改变多边形中节点的顺序来进一步改进布局。为了证明我们的技术和系统的有效性,我们将它们应用于社交网络和基因数据集。
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