SPECTRAL DISTRIBUTIONS OF ADJACENCY AND LAPLACIAN MATRICES OF RANDOM GRAPHS

被引:68
|
作者
Ding, Xue [1 ,2 ]
Jiang, Tiefeng [2 ]
机构
[1] Jilin Univ, Sch Math, Changchun 130023, Peoples R China
[2] Univ Minnesota, Sch Stat, Minneapolis, MN 55455 USA
来源
ANNALS OF APPLIED PROBABILITY | 2010年 / 20卷 / 06期
关键词
Random graph; random matrix; adjacency matrix; Laplacian matrix; largest eigenvalue; spectral distribution; semi-circle law; free convolution; SAMPLE COVARIANCE MATRICES; EIGENVALUE DISTRIBUTION; LARGE DEVIATIONS; DENSITY; STATES;
D O I
10.1214/10-AAP677
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
In this paper, we investigate the spectral properties of the adjacency and the Laplacian matrices of random graphs. We prove that: (i) the law of large numbers for the spectral norms and the largest eigenvalues of the adjacency and the Laplacian matrices; (ii) under some further independent conditions, the normalized largest eigenvalues of the Laplacian matrices are dense in a compact interval almost surely; (iii) the empirical distributions of the eigenvalues of the Laplacian matrices converge weakly to the free convolution of the standard Gaussian distribution and the Wigner's semi-circular law; (iv) the empirical distributions of the eigenvalues of the adjacency matrices converge weakly to the Wigner's semi-circular law.
引用
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页码:2086 / 2117
页数:32
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