度量图的内在切赫与持续畸变距离的关系

Ellen Gasparovic, Maria Gommel, Emilie Purvine, R. Sazdanovic, Bei Wang, Yusu Wang, Lori Ziegelmeier
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引用次数: 4

摘要

度量图是有意义的对象,用于建模在许多现实世界应用中出现的复杂结构,例如道路网络、河流系统、地震断层、血管和星系中的丝状结构。为了在比较的背景下研究度量图,我们感兴趣的是确定一对任意有限度量图之间的两个基于拓扑的距离的相对判别能力:持久失真距离和固有切赫距离。我们明确地展示了如何计算两个度量图之间的内在切赫距离,这仅仅基于图的最短循环系统的知识。我们的主要定理在一个图是花束图而另一个图是任意图的情况下,建立了内在切赫和持久畸变距离之间的不等式。当两个图都是通过环和边的楔形和构造时,这种关系也成立。
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The Relationship Between the Intrinsic Cech and Persistence Distortion Distances for Metric Graphs
Metric graphs are meaningful objects for modeling complex structures that arise in many real-world applications, such as road networks, river systems, earthquake faults, blood vessels, and filamentary structures in galaxies. To study metric graphs in the context of comparison, we are interested in determining the relative discriminative capabilities of two topology-based distances between a pair of arbitrary finite metric graphs: the persistence distortion distance and the intrinsic Cech distance. We explicitly show how to compute the intrinsic Cech distance between two metric graphs based solely on knowledge of the shortest systems of loops for the graphs. Our main theorem establishes an inequality between the intrinsic Cech and persistence distortion distances in the case when one of the graphs is a bouquet graph and the other is arbitrary. The relationship also holds when both graphs are constructed via wedge sums of cycles and edges.
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来源期刊
CiteScore
0.80
自引率
0.00%
发文量
4
审稿时长
>12 weeks
期刊介绍: The International Journal of Computational Geometry & Applications (IJCGA) is a quarterly journal devoted to the field of computational geometry within the framework of design and analysis of algorithms. Emphasis is placed on the computational aspects of geometric problems that arise in various fields of science and engineering including computer-aided geometry design (CAGD), computer graphics, constructive solid geometry (CSG), operations research, pattern recognition, robotics, solid modelling, VLSI routing/layout, and others. Research contributions ranging from theoretical results in algorithm design — sequential or parallel, probabilistic or randomized algorithms — to applications in the above-mentioned areas are welcome. Research findings or experiences in the implementations of geometric algorithms, such as numerical stability, and papers with a geometric flavour related to algorithms or the application areas of computational geometry are also welcome.
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