Perturbation of Fiedler vector: interest for graph measures and shape analysis

J. Lefévre, Justine Fraize, D. Germanaud
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引用次数: 1

Abstract

In this paper we investigate some properties of the Fiedler vector, the so-called first non-trivial eigenvector of the Laplacian matrix of a graph. There are important results about the Fiedler vector to identify spectral cuts in graphs but far less is known about its extreme values and points. We propose a few results and conjectures in this direction. We also bring two concrete contributions, i) by defining a new measure for graphs that can be interpreted in terms of extremality (inverse of centrality), ii) by applying a small perturbation to the Fiedler vector of cerebral shapes such as the corpus callosum to robustify their parameterization.
菲尔德勒矢量的摄动:对图形测度和形状分析的兴趣
本文研究了图的拉普拉斯矩阵的第一非平凡特征向量Fiedler向量的一些性质。关于费德勒矢量识别图中的谱切有重要的结果,但对其极值和点的了解甚少。我们在这个方向上提出了一些结果和猜想。我们还带来了两个具体的贡献,i)通过定义一个可以用极值(中心性的逆)来解释的图的新度量,ii)通过对大脑形状(如胼胝体)的费德勒矢量施加一个小的扰动来增强它们的参数化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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