Edge Metrics for Visual Graph Analytics: A Comparative Study
Edge Metrics for Visual Graph Analytics: A Comparative Study
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可视化图形分析的边缘指标：比较研究
DOI：
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
A. Sallaberry
中科院分区：
文献类型：
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作者：
G. Melançon;A. Sallaberry
可视化图形分析绝对依赖于使用节点和边缘指标来识别图形中的显着属性。大多数情况下，这些指标会转化为有用的视觉提示，或者用于在查询图表时以交互方式过滤掉图表的某些部分。多年来，来自不同应用领域的分析师设计了满足特定需求的指标。图分析，有时也称为网络科学，最近发展为跨学科领域，开发由生物信息学、社交网络分析、网络图等众多应用领域共享的模型。[4] [10]。因此，我们最终在文献中找到了旨在实现相似目标的各种指标；不同的名称和分析描述通常隐藏了源自不同领域的两个指标之间的相似性。我们调查了图表的边缘指标列表，并比较它们的相对价值和行为，努力将它们组织成一个分类法，并强调每个图表的真正成分，无论其来源如何。
Visual graph analytics definitely relies on the use of node and edge metrics to identify salient properties in graphs. Most often, these metrics are turned into useful visual cues, or are used to interactively filter out parts of a graph while querying it, for instance. Along the years, analysts coming from different application domains have designed metrics to serve specific needs. Graph analytics, sometimes also called network science, recently developed as a cross-discipline field developing models shared by numerous application domains such as bio-informatics, social network analysis, web graphs, etc.[4] [10]. As a consequence, we end up finding various metrics in the literature aiming at similar goals; different names and analytics description often hide similarity between two metrics that originated from different fields. We survey a list of edge metrics for graphs and compare their relative value and behaviour, in an effort to organize them into a taxonomy and underline the genuine ingredients in each of them disregarding their origin.