Characterization of random matrix eigenvectors for stochastic block model

The eigenvalue spectrum of the adjacency matrix of Stochastic Block Model (SBM) consists of two parts: a finite discrete set of dominant eigenvalues and a continuous bulk of eigenvalues. We characterize analytically the eigenvectors corresponding to the continuous part: the bulk eigenvectors. For symmetric SBM adjacency matrices, the eigenvectors are shown to satisfy two key properties. A modified spectral function of the eigenvalues, depending on the eigenvectors, converges to the eigenvalue spectrum. Its fluctuations around this limit converge to a Gaussian process different from a Brownian bridge. This latter fact disproves that the bulk eigenvectors are Haar distributed.

[1]  Laura Cottatellucci,et al.  Spectral properties of random matrices for stochastic block model , 2015, 2015 13th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt).

[2]  Patrick J. Wolfe,et al.  A Spectral Framework for Anomalous Subgraph Detection , 2014, IEEE Transactions on Signal Processing.

[3]  David J. Marchette,et al.  A central limit theorem for scaled eigenvectors of random dot product graphs , 2013, 1305.7388.

[4]  Carey E. Priebe,et al.  Consistent Adjacency-Spectral Partitioning for the Stochastic Block Model When the Model Parameters Are Unknown , 2012, SIAM J. Matrix Anal. Appl..

[5]  Guangming Pan,et al.  Limiting Behavior of Eigenvectors of Large Wigner Matrices , 2012 .

[6]  Carey E. Priebe,et al.  A consistent dot product embedding for stochastic blockmodel graphs , 2011 .

[7]  Terence Tao,et al.  Random matrices: Universal properties of eigenvectors , 2011, 1103.2801.

[8]  Bin Yu,et al.  Spectral clustering and the high-dimensional stochastic blockmodel , 2010, 1007.1684.

[9]  R. Bass,et al.  Review: P. Billingsley, Convergence of probability measures , 1971 .

[10]  A. Guionnet,et al.  An Introduction to Random Matrices , 2009 .

[11]  G. Pan,et al.  On asymptotics of eigenvectors of large sample covariance matrix , 2007, 0708.1720.

[12]  J. W. Silverstein,et al.  No eigenvalues outside the support of the limiting spectral distribution of large-dimensional sample covariance matrices , 1998 .

[13]  P. Spreij Probability and Measure , 1996 .

[14]  V. Girko,et al.  A necessary and sufficient conditions for the semicircle law , 1994 .

[15]  J. W. Silverstein Weak Convergence of random functions defined by the eigenvectors of sample covariance matrices , 1990 .