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We compare the spectrum and the localisation properties of the eigenmodes of the Laplacian and the adjacency matrix of 2D random geometric graphs, using numerical diagonalization of these matrices for different system sizes and…

Disordered Systems and Neural Networks · Physics 2026-04-01 Luca Schaefer , Barbara Drossel

Using exact numerical diagonalization, we investigate localization in two classes of random matrices corresponding to random graphs. The first class comprises the adjacency matrices of Erdos-Renyi (ER) random graphs. The second one…

Statistical Mechanics · Physics 2014-01-10 Frantisek Slanina

We study the properties of random graphs where for each vertex a {\it neighbourhood} has been previously defined. The probability of an edge joining two vertices depends on whether the vertices are neighbours or not, as happens in Small…

Disordered Systems and Neural Networks · Physics 2009-11-10 Sebastian Risau-Gusman

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

The spectral properties of the Laplacian operator on ``small-world'' lattices, that is mixtures of unidimensional chains and random graphs structures are investigated numerically and analytically. A transfer matrix formalism including a…

Disordered Systems and Neural Networks · Physics 2009-10-31 Remi Monasson

Using our previously published algorithm, we analyze the eigenvectors of the generalized Laplacian for two metric graphs occurring in practical applications. As expected, localization of an eigenvector is rare and the network should be…

Mathematical Physics · Physics 2023-02-08 H. Kravitz , M. Brio , J. -G. Caputo

Networks are often studied using the eigenvalues of their adjacency matrix, a powerful mathematical tool with a wide range of applications. Since in real systems the exact graph structure is not known, researchers resort to random graphs to…

Spectral Theory · Mathematics 2020-01-30 Pau Vilimelis Aceituno

We study phenomena where some eigenvectors of a graph Laplacian are largely confined in small subsets of the graph. These localization phenomena are similar to those generally termed Anderson Localization in the Physics literature, and are…

Systems and Control · Electrical Eng. & Systems 2025-04-08 Poorva Shukla , Bassam Bamieh

Consider a random regular graph of fixed degree $d$ with $n$ vertices. We study spectral properties of the adjacency matrix and of random Schr\"odinger operators on such a graph as $n$ tends to infinity. We prove that the integrated density…

Mathematical Physics · Physics 2014-05-09 Leander Geisinger

We study spectra and localization properties of Euclidean random matrices. The problem is approximately mapped onto that of a matrix defined on a random graph. We introduce a powerful method to find the density of states and the…

Statistical Mechanics · Physics 2009-11-10 S. Ciliberti , T. S. Grigera , V. Martin-Mayor , G. Parisi , P. Verrocchio

In this work, we study some statistical properties of the extreme eigenstates of the randomly-weighted adjacency matrices of random graphs. We focus on two random graph models: Erd\H{o}s-R\'{e}nyi (ER) graphs and random geometric graphs…

Disordered Systems and Neural Networks · Physics 2025-06-17 C. T Martínez Martínez , J. A. Méndez Bermúdez

We study the spectral properties of the adjacency matrix in the giant connected component of Erd\"os-R\'enyi random graphs, with average connectivity $p$ and randomly distributed hopping amplitudes. By solving the self-consistent cavity…

Disordered Systems and Neural Networks · Physics 2024-11-14 Leticia F. Cugliandolo , Grégory Schehr , Marco Tarzia , Davide Venturelli

We investigate the distribution of eigenvalues of weighted adjacency matrices from a specific ensemble of random graphs. We distribute $N$ vertices across a fixed number $\kappa$ of components, with asymptotically $\alpha_j \dot N$ vertices…

Mathematical Physics · Physics 2024-09-30 Valentin Vengerovsky

In this paper we address the problem of understanding the success of algorithms that organize patches according to graph-based metrics. Algorithms that analyze patches extracted from images or time series have led to state-of-the art…

Data Analysis, Statistics and Probability · Physics 2011-07-05 Kye M. Taylor , Francois G. Meyer

We examine the empirical distribution of the eigenvalues and the eigenvectors of adjacency matrices of sparse regular random graphs. We find that when the degree sequence of the graph slowly increases to infinity with the number of…

Probability · Mathematics 2012-10-15 Ioana Dumitriu , Soumik Pal

We study random graphs with latent geometric structure, where the probability of each edge depends on the underlying random positions corresponding to the two endpoints. We focus on the setting where this conditional probability is a…

Probability · Mathematics 2021-11-01 Suqi Liu , Miklos Z. Racz

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 study eigenvalue distribution of the adjacency matrix $A^{(N,p, \alpha)}$ of weighted random bipartite graphs $\Gamma= \Gamma_{N,p}$. We assume that the graphs have $N$ vertices, the ratio of parts is $\frac{\alpha}{1-\alpha}$ and the…

Mathematical Physics · Physics 2013-12-03 Valentin Vengerovsky

We analyze gene co-expression network under the random matrix theory framework. The nearest neighbor spacing distribution of the adjacency matrix of this network follows Gaussian orthogonal statistics of random matrix theory (RMT). Spectral…

Molecular Networks · Quantitative Biology 2015-05-18 Sarika Jalan , Norbert Solymosi , Gabör Vattay , Baowen Li

We give a delocalization estimate for eigenfunctions of the discrete Laplacian on large $d+1$-regular graphs, showing that any subset of the graph supporting $\epsilon$ of the $L^2$ mass of an eigenfunction must be large. For graphs…

Dynamical Systems · Mathematics 2009-12-17 Shimon Brooks , Elon Lindenstrauss
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