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A hybrid space-filling and force-directed layout method for visualizing multiple-category graphs

机译:用于可视化多类别图的混合空间填充和力导向布局方法

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Many graphs used in real-world applications consist of nodes belonging to more than one category. We call such graph “multiple-category graphs”. Social networks are typical examples of multiple-category graphs: nodes are persons, links are friendships, and categories are communities that the persons belong to. It is often helpful to visualize both connectivity and categories of the graphs simultaneously. In this paper, we present a new visualization technique for multiple-category graphs. The technique firstly constructs hierarchical clusters of the nodes based on both connectivity and categories. It then places the nodes by a new hybrid space-filling and force-directed layout algorithm to clearly display both connectivity and category information. We show layout results using our hybrid method and compare it with other methods, and present a case study using an active biological network dataset.
机译:实际应用中使用的许多图形由属于多个类别的节点组成。我们称这种图为“多类别图”。社交网络是多类别图的典型示例:节点是人,链接是友谊,类别是人所属的社区。同时显示图表的连通性和类别通常很有帮助。在本文中,我们提出了一种用于多类别图的新可视化技术。该技术首先基于连接性和类别构建节点的层次集群。然后,它通过新的混合空间填充和力导向布局算法放置节点,以清晰显示连接性和类别信息。我们使用混合方法显示布局结果,并将其与其他方法进行比较,并使用活跃的生物网络数据集进行案例研究。

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