多尺度平面图生成
Multiscale Planar Graph Generation
摘要：
对物理、生物和社会现象的网络表征的研究可以帮助我们更好地理解其网络的结构和功能动态，并制定这些现象的预测模型。然而，由于诸如收集网络数据所需的成本和努力以及这些数据对盗窃和滥用的敏感性等因素导致的真实世界网络数据的稀缺性，工程师和研究人员经常依赖于合成数据进行仿真、假设检验、决策制定和算法工程。基础设施网络的一个重要特征是它们可以嵌入到平面中，因此为了模拟这些系统，我们需要也是平面的真实网络。虽然目前可用的合成网络生成器可以对表现出真实性的网络进行建模，但它们不能保证或实现平面性。因此，在本文中，我们提出了一个灵活的算法，可以合成现实的网络是平面的。该方法遵循多尺度随机编辑方法，生成给定平面图的粗化网络的层次结构，并在层次结构中的各个级别引入编辑。该方法保留了最小的偏差，包括网络的平面性的结构特性，同时在多个尺度上引入现实的变化。
英文摘要：
The study of network representations of physical, biological, and social phenomena can help us better understand the structural and functional dynamics of their networks and formulate predictive models of these phenomena. However, due to the scarcity of real-world network data owing to factors such as cost and effort required in collection of network data and the sensitivity of this data towards theft and misuse, engineers and researchers often rely on synthetic data for simulations, hypothesis testing, decision making, and algorithm engineering. An important characteristic of infrastructure networks such as roads, water distribution and other utility systems is that they can be embedded in a plane, therefore to simulate these system we need realistic networks which are also planar. While the currently-available synthetic network generators can model networks that exhibit realism, they do not guarantee or achieve planarity. Therefore, in this paper we present a flexible algorithm that can synthesize realistic networks that are planar. The method follows a multi-scale randomized editing approach generating a hierarchy of coarsened networks of a given planar graph and introducing edits at various levels in the hierarchy. The method preserves the structural properties with minimal bias including the planarity of the network, while introducing realistic variability at multiple scales.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
