We review the problem of how to compute the spectral density of sparse symmetric random matrices, i.e. weighted adjacency matrices of undirected graphs. Starting from the Edwards-Jones formula, we illustrate the milestones of this line of research, including the pioneering work of Bray and Rodgers using replicas. We focus first on the cavity method, showing that it quickly provides the correct recursion equations both for single instances and at the ensemble level. We also describe an alternative replica solution that proves to be equivalent to the cavity method. Both the cavity and the replica derivations allow us to obtain the spectral density via the solution of an integral equation for an auxiliary probability density function. We show that this equation can be solved using a stochastic population dynamics algorithm, and we provide its implementation. In this formalism, the spectral density is naturally written in terms of a superposition of local contributions from nodes of given degree, whose role is thoroughly elucidated. This paper does not contain original material, but rather gives a pedagogical overview of the topic. It is indeed addressed to students and researchers who consider entering the field. Both the theoretical tools and the numerical algorithms are discussed in detail, highlighting conceptual subtleties and practical aspects.
Cited by 4
Baron et al., Eigenvalues of Random Matrices with Generalized Correlations: A Path Integral Approach
Phys. Rev. Lett. 128, 120601 (2022) [Crossref]
Tarzia, Fully localized and partially delocalized states in the tails of Erdös-Rényi graphs in the critical regime
Phys. Rev. B 105, 174201 (2022) [Crossref]
Cicuta et al., Sparse random block matrices
J. Phys. A: Math. Theor. 55, 175202 (2022) [Crossref]
Tapias et al., Localization properties of the sparse Barrat-Mézard trap model
Phys. Rev. E 105, 054109 (2022) [Crossref]
Authors / Affiliation: mappings to Contributors and OrganizationsSee all Organizations.
- 1 Vito A. R. Susca,
- 1 Pierpaolo Vivo,
- 1 Reimer Kühn