网络镜头:使用视觉过滤的多元网络的交互式探索

Ilir Jusufi, Yang Dingjie, A. Kerren
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引用次数: 36

摘要

网络被广泛应用于关系数据建模,关系数据通常由数千个节点和边组成。这类数据本身就意味着对其可视化的挑战,因为如果使用传统的节点链接图,很难避免网络元素的混乱。此外,现实生活中的网络数据集通常表示具有大量需要可视化的附加属性的对象,例如在软件工程、社会网络分析或生物化学中。在本文中,我们提出了一种新颖的方法,称为网络透镜,在底层网络的上下文中可视化这些属性。我们的网络镜头实现是一个交互式工具,它扩展了所谓的神奇镜头的思想,这样用户就可以通过指定不同的属性和选择合适的视觉表示来交互式地构建和组合各种镜头。
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The Network Lens: Interactive Exploration of Multivariate Networks Using Visual Filtering
Networks are widely used in modeling relational data often comprised of thousands of nodes and edges. This kind of data alone implies a challenge for its visualization as it is hard to avoid clutter of network elements if using traditional node-link diagrams. Moreover, real-life network data sets usually represent objects with a large number of additional attributes that need to be visualized, such as in software engineering, social network analysis, or biochemistry. In this paper, we present a novel approach, called Network Lens, to visualize such attributes in context of the underlying network. Our implementation of the Network Lens is an interactive tool that extends the idea of so-called magic lenses in such a way that users can interactively build and combine various lenses by specifying different attributes and selecting suitable visual representations.
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