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Random incidence matrices: Moments of the spectral density

机译:随机入射矩阵:谱密度矩

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We study numerically and analytically the spectrum of incidence matrices of random labeled graphs on N vertices: any pair of vertices is connected by an edge with probability p. We give two algorithms to compute the moments of the eigenvalue distribution as explicit polynomials in iv and p. For large N and fixed p, the spectrum contains a large eigenvalue at Np and a semicircle of "small" eigenvalues. For large N and fixed average connectivity pN (dilute or sparse random mall ices limit) we show that the spectrum always contains a discrete component. An anomaly in the spectrum near eigenvalue 0 for connectivity close to c is observed. We develop recursion relations to compute the moments as explicit polynomials in pN. Their growth is slow enough so that they determine the spectrum. The extension of our methods to the Laplacian matrix is given in Appendix. [References: 26]
机译:我们在数字上和分析上研究N个顶点上随机标记图的入射矩阵的谱:任意一对顶点之间的连接概率为p。我们给出了两种算法来计算特征值分布的矩,作为iv和p中的显式多项式。对于较大的N和固定的p,频谱在Np处包含一个较大的特征值,并包含一个“较小”特征值的半圆。对于较大的N和固定的平均连通性pN(稀疏或稀疏的随机冰块极限),我们表明频谱始终包含离散分量。观察到在接近特征值0的光谱中存在接近c的连接异常。我们开发了递归关系以将矩计算为pN中的显式多项式。它们的生长速度足够慢,因此可以确定频谱。我们的方法扩展到拉普拉斯矩阵在附录中给出。 [参考:26]

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