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.
机构:
Anhui Univ, Sch Math Sci, Hefei 230039, Peoples R China
Anqing Teachers Coll, Sch Math & Computat Sci, Anqing 246011, Peoples R ChinaAnhui Univ, Sch Math Sci, Hefei 230039, Peoples R China
Ye, Miao-Lin
Fan, Yi-Zheng
论文数: 0引用数: 0
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机构:
Anhui Univ, Sch Math Sci, Hefei 230039, Peoples R ChinaAnhui Univ, Sch Math Sci, Hefei 230039, Peoples R China
Fan, Yi-Zheng
Wang, Hai-Feng
论文数: 0引用数: 0
h-index: 0
机构:
Anqing Teachers Coll, Sch Math & Computat Sci, Anqing 246011, Peoples R ChinaAnhui Univ, Sch Math Sci, Hefei 230039, Peoples R China