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Multi-focus and Multi-window Techniques for Interactive Network Exploration

机译:交互式网络探索的多焦点和多窗口技术

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Networks analysts often need to compare nodes in different parts of a network. When zoomed to fit a computer screen, the detailed structure and node labels of even a moderately-sized network (say, with 500 nodes) can become invisible or difficult to read. Still, the coarse network structure typically remains visible, and helps orient an analyst's zooming, scrolling, and panning operations. These operations are very useful when studying details and reading node labels, but in the process of zooming in on one network region, an analyst may lose track of details elsewhere. To address such problems, we present in this paper multi-focus and multi-window techniques that improve interactive exploration of networks. Based on an analyst's selection of focus nodes, our techniques partition and selectively zoom in on network details, including node labels, close to the focus nodes. Detailed data associated with the zoomed-in nodes can thus be more easily accessed and inspected. The approach enables a user to simultaneously focus on and analyze multiple node neighborhoods while keeping the full network structure in view. We demonstrate our technique by showing how it supports interactive debugging of a Bayesian network model of an electrical power system. In addition, we show that it can simplify visual analysis of an electrical power network as well as a medical Bayesian network.
机译:网络分析师通常需要比较网络不同部分中的节点。当缩放以适合计算机屏幕时,即使是中等规模的网络(例如,具有500个节点)的详细结构和节点标签也可能变得不可见或难以阅读。尽管如此,粗略的网络结构通常仍然可见,并有助于确定分析人员的缩放,滚动和平移操作的方向。这些操作在研究详细信息和读取节点标签时非常有用,但是在放大一个网络区域的过程中,分析师可能会丢失其他位置的详细信息。为了解决这些问题,我们在本文中提出了改进网络交互探索的多焦点和多窗口技术。根据分析人员对焦点节点的选择,我们的技术进行分区并有选择地放大靠近焦点节点的网络详细信息,包括节点标签。因此,与放大的节点关联的详细数据可以更容易地访问和检查。该方法使用户可以同时关注和分析多个节点邻域,同时保持整个网络结构的可见性。我们通过展示如何支持电力系统的贝叶斯网络模型的交互式调试来展示我们的技术。此外,我们表明它可以简化对电力网络以及医学贝叶斯网络的可视化分析。

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