【24h】

Orthogonal Eigenvector Matrix of the Laplacian

机译:拉普拉斯算子的正交特征向量矩阵

获取原文

摘要

The orthogonal eigenvector matrix Z of the Laplacian matrix of a graph with N nodes is studied rather than its companion X of the adjacency matrix, because for the Laplacian matrix, the eigenvector matrix Z corresponds to the adjacency companion X of a regular graph, whose properties are easier. In particular, the column sum vector of Z (which we call the fundamental weight vector w) is, for a connected graph, proportional to the basic vector e = (0, 0, . . . , 1), so that more information about the speclics of the graph is contained in the row sum of Z (which we call the dual fundamental weight vector Φ). Since little is known about Z (or X), we have tried to understand simple properties of Z such as the number of zeros, the sum of elements, the maximum and minimum element and properties of Φ. For the particular class of Erdos-Rényi random graphs, we found that a product of a Gaussian and a super-Gaussian distribution approximates accurately the distribution of Φ, a uniformly at random chosen component of the dual fundamental weight vector of Z.
机译:研究具有N个节点的图的拉普拉斯矩阵的正交特征向量矩阵Z而不是邻接矩阵的伴随X,因为对于拉普拉斯矩阵,特征向量矩阵Z对应于规则图的邻接伴随X,更容易。特别是,对于一个连通图,Z的列和向量(我们称为基本权重向量w)与基本向量e =(0,0,...,1)成正比,因此,有关图的特征包含在Z的行总和中(我们称其为对偶基本权重向量Φ)。由于对Z(或X)知之甚少,我们试图了解Z的简单性质,例如零的数量,元素之和,最大和最小元素以及Φ的性质。对于特定类别的Erdos-Rényi随机图,我们发现高斯分布和超高斯分布的乘积可以精确地近似近似Φ的分布。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号