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We analyse the eigenvectors of the adjacency matrix of a random inhomogeneous graph constructed from a specified degree sequence. We assume that the empirical degree sequence has bounded mean and variance. We show that near the edges of the…

Probability · Mathematics 2026-04-14 Thomas Buc-d'Alché , Antti Knowles

We consider the uniform approximation of the smallest eigenvalue of a large parameter-dependent Hermitian matrix by that of a smaller counterpart obtained through projections. The projection subspaces are constructed iteratively by means of…

Numerical Analysis · Mathematics 2026-01-16 Mattia Manucci , Emre Mengi , Nicola Guglielmi

We study the statistics of the local resolvent and non-ergodic properties of eigenvectors for a generalised Rosenzweig-Porter $N\times N$ random matrix model, undergoing two transitions separated by a delocalised non-ergodic phase.…

Disordered Systems and Neural Networks · Physics 2016-09-29 Davide Facoetti , Pierpaolo Vivo , Giulio Biroli

Network representations are useful for describing the structure of a large variety of complex systems. Although most studies of real-world networks suppose that nodes are connected by only a single type of edge, most natural and engineered…

Physics and Society · Physics 2020-08-05 Rubén J. Sánchez-García , Emanuele Cozzo , Yamir Moreno

Degree correlation plays a crucial role in studying network structures; however, its varied forms pose challenges to understanding its impact on network dynamics. This study devised a method that uses eigenvalue decomposition to…

Physics and Society · Physics 2023-11-22 Satoru Morita

In an attempt to characterize the structure of eigenvectors of random regular graphs, we investigate the correlations between the components of the eigenvectors associated to different vertices. In addition, we provide numerical…

Mathematical Physics · Physics 2009-11-13 Yehonatan Elon

We investigate the real eigenvalues of asymmetric Wishart matrices of size $N$, indexed by the rectangular parameter $\nu \in \mathbb{N}$ and the non-Hermiticity parameter $\tau \in [0,1]$. The rectangular parameter $\nu$ is either fixed or…

Probability · Mathematics 2025-03-20 Sung-Soo Byun , Kohei Noda

The nonbacktracking matrix, and the related nonbacktracking centrality (NBC) play a crucial role in models of percolation-type processes on networks, such as non-recurrent epidemics. Here we study the localization of NBC in infinite sparse…

Physics and Society · Physics 2023-08-29 G. Timár , S. N. Dorogovtsev , J. F. F. Mendes

We present first-order perturbation analysis of a simple eigenvalue and the corresponding right and left eigenvectors of a general square matrix, not assumed to be Hermitian or normal. The eigenvalue result is well known to a broad…

Numerical Analysis · Mathematics 2019-06-04 Anne Greenbaum , Ren-cang Li , Michael L. Overton

This book is about solving matrix nearness problems that are related to eigenvalues or singular values or pseudospectra. These problems arise in great diversity in various fields, be they related to dynamics, as in questions of robust…

Numerical Analysis · Mathematics 2025-07-29 Nicola Guglielmi , Christian Lubich

We consider two-point non-self-adjoint boundary eigenvalue problems for linear matrix differential operators. The coefficient matrices in the differential expressions and the matrix boundary conditions are assumed to depend analytically on…

Mathematical Physics · Physics 2010-04-20 Oleg N. Kirillov

We derive universal entanglement entropy and Schmidt eigenvalue behaviors for the eigenstates of two quantum chaotic systems coupled with a weak interaction. The progression from a lack of entanglement in the noninteracting limit to the…

Quantum Physics · Physics 2018-09-19 Steven Tomsovic , Arul Lakshminarayan , Shashi C. L. Srivastava , Arnd Bäcker

Many observables of brain dynamics appear to be optimized for computation. Which connectivity structures underlie this fine-tuning? We propose that many of these structures are naturally encoded in the space that more directly relates to…

Disordered Systems and Neural Networks · Physics 2023-09-18 Lorenzo Tiberi , David Dahmen , Moritz Helias

Inspired by the importance of inhibitory and excitatory couplings in the brain, we analyze the largest eigenvalue statistics of random networks incorporating such features. We find that the largest real part of eigenvalues of a network,…

Disordered Systems and Neural Networks · Physics 2013-04-30 Sanjiv Kumar Dwivedi , Sarika Jalan

We first consider the problem of approximating a few eigenvalues of a rational matrix-valued function closest to a prescribed target. It is assumed that the proper rational part of the rational matrix-valued function is expressed in the…

Numerical Analysis · Mathematics 2022-01-10 Rifqi Aziz , Emre Mengi , Matthias Voigt

We consider highly heterogeneous random networks with symmetric interactions in the limit of high connectivity. A key feature of this system is that the spectral density of the corresponding ensemble exhibits a divergence within the bulk.…

Disordered Systems and Neural Networks · Physics 2023-11-29 Diego Tapias , Peter Sollich

Spectral correlations in unitary invariant, non-Gaussian ensembles of large random matrices possessing an eigenvalue gap are studied within the framework of the orthogonal polynomial technique. Both local and global characteristics of…

Statistical Mechanics · Physics 2009-10-30 E. Kanzieper , V. Freilikher

We consider the random matrix ensemble with an external source \[ \frac{1}{Z_n} e^{-n \Tr({1/2}M^2 -AM)} dM \] defined on $n\times n$ Hermitian matrices, where $A$ is a diagonal matrix with only two eigenvalues $\pm a$ of equal…

Mathematical Physics · Physics 2009-11-10 Pavel M. Bleher , Arno B. J. Kuijlaars

We consider real symmetric or complex hermitian random matrices with correlated entries. We prove local laws for the resolvent and universality of the local eigenvalue statistics in the bulk of the spectrum. The correlations have fast decay…

Probability · Mathematics 2018-03-01 Oskari Ajanki , Laszlo Erdos , Torben Krüger

Random graphs defined by an occurrence probability that is invariant under node aggregation have been identified recently in the context of network renormalization. The invariance property requires that edges are drawn with a specific…

Spectral Theory · Mathematics 2025-09-18 Alessio Catanzaro , Rajat Subhra Hazra , Diego Garlaschelli