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首页> 外文会议>International symposium on graph drawing >Peacock Bundles: Bundle Coloring for Graphs with Globality-Locality Trade-Off
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Peacock Bundles: Bundle Coloring for Graphs with Globality-Locality Trade-Off

机译：孔雀束：具有全局性-局部性权衡的图形的束着色

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Bundling of graph edges (node-to-node connections) is a common technique to enhance visibility of overall trends in the edge structure of a large graph layout, and a large variety of bundling algorithms have been proposed. However, with strong bundling, it becomes hard to identify origins and destinations of individual edges. We propose a solution: we optimize edge coloring to differentiate bundled edges. We quantify strength of bundling in a flexible pairwise fashion between edges, and among bundled edges, we quantify how dissimilar their colors should be by dissimilarity of their origins and destinations. We solve the resulting nonlinear optimization, which is also interpretable as a novel dimensionality reduction task. In large graphs the necessary compromise is whether to differentiate colors sharply between locally occurring strongly bundled edges ("local bundles"), or also between the weakly bundled edges occurring globally over the graph ("global bundles"); we allow a user-set global-local tradeoff. We call the technique "peacock bundles" . Experiments show the coloring clearly enhances comprehensibility of graph layouts with edge bundling.
机译：图边缘的捆绑（节点到节点的连接）是一种常见的技术，可以增强大型图布局的边缘结构中总体趋势的可见性，并且已经提出了多种捆绑算法。但是，通过强力捆绑，很难识别各个边的起点和终点。我们提出一个解决方案：我们优化边缘着色以区分捆绑的边缘。我们以灵活的成对方式对边缘之间的捆绑强度进行量化，而在捆绑的边缘之间，我们通过源和目的地的差异来量化其颜色的差异。我们解决了由此产生的非线性优化问题，这也可以解释为一种新颖的降维任务。在大型图中，必要的折衷方案是在区域之间发生的强烈捆绑边缘（“局部捆绑”）之间，还是在图上全局出现的各个弱捆绑边缘（“全局捆绑”）之间进行鲜明的颜色区分;我们允许用户设置全局-本地权衡。我们称该技术为“孔雀束”。实验表明，着色显着增强了带有边缘捆绑的图形布局的可理解性。

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