King's College London

Research portal

Second largest Eigenpair Statistics for Sparse Graphs

Research output: Contribution to journalArticlepeer-review

Original languageEnglish
JournalJournal Of Physics A-Mathematical And Theoretical
Accepted/In press5 Nov 2020


  • NewPaper_RE_Final

    NewPaper_RE_Final.pdf, 1.72 MB, application/pdf

    Uploaded date:09 Nov 2020

    Version:Accepted author manuscript

King's Authors


We develop a formalism to compute the statistics of the second largest eigenpair of weighted sparse graphs with $N\gg 1$ nodes, finite mean connectivity and bounded maximal degree, in cases where the top eigenpair statistics is known. The problem can be cast in terms of optimisation of a quadratic form on the sphere with a fictitious temperature, after a suitable deflation of the original matrix model. We use the cavity and replica methods to find the solution in terms of self-consistent equations for auxiliary probability density functions, which can be solved by an improved population dynamics algorithm enforcing eigenvector orthogonality on-the-fly. The analytical results are in perfect agreement with numerical diagonalisation of large (weighted) adjacency matrices, focussing on the cases of random regular and Erd\H{o}s-R\'enyi graphs. We further analyse the case of sparse Markov transition matrices for unbiased random walks, whose second largest eigenpair describes the non-equilibrium mode with the largest relaxation time. We also show that the population dynamics algorithm with population size $N_P$ does not actually capture the thermodynamic limit $N\to\infty$ as commonly assumed: the accuracy of the population dynamics algorithm has a strongly non-monotonic behaviour as a function of $N_P$, thus implying that an optimal size $N_P^\star=N_P^\star(N)$ must be chosen to best reproduce the results from numerical diagonalisation of graphs of finite size $N$.

Download statistics

No data available

View graph of relations

© 2020 King's College London | Strand | London WC2R 2LS | England | United Kingdom | Tel +44 (0)20 7836 5454