Spectral renormalization group theory on networks
arXiv:1107.3457 · doi:10.1088/1742-6596/319/1/012007
Abstract
Discrete amorphous materials are best described in terms of arbitrary networks which can be embedded in three dimensional space. Investigating the thermodynamic equilibrium as well as non-equilibrium behavior of such materials around second order phase transitions call for special techniques. We set up a renormalization group scheme by expanding an arbitrary scalar field living on the nodes of an arbitrary network, in terms of the eigenvectors of the normalized graph Laplacian. The renormalization transformation involves, as usual, the integration over the more "rapidly varying" components of the field, corresponding to eigenvectors with larger eigenvalues, and then rescaling. The critical exponents depend on the particular graph through the spectral density of the eigenvalues.
17 pages, 3 figures, presented at the Continuum Models and Discrete Systems (CMDS-12), 21-25 Feb 2011, Saha Institute of Nuclear Physics, Kolkata, India
References in corpus (4)
- Critical phenomena in complex networks
- Direct Estimate of the Static Length-Scale Accompanying the Glass Transition
- Griffiths singularities and algebraic order in the exact solution of an Ising model on a fractal modular network
- Randomness-Induced Redistribution of Vibrational Frequencies in Amorphous Solids
Cited by in corpus (10)
- PCA meets RG
- Simplicial complexes: higher-order spectral dimension and dynamics
- The spectral dimension of simplicial complexes: a renormalization group theory
- The higher-order spectrum of simplicial complexes: a renormalization group approach
- Network Renormalization
- Signal detection in nearly continuous spectra and symmetry breaking
- Laplacian Renormalization Group: An introduction to heterogeneous coarse-graining
- Spectral Renormalization Group for the Gaussian model and theory on non-spatial networks
- Local topological moves determine global diffusion properties of hyperbolic higher-order networks
- Analysis of the inference of ratings and rankings in complex networks using discrete exterior calculus on higher--order networks