Strength distribution in gradient networks
Luciano da Fontoura Costa
Abstract
This article describes a gradient complex network model whose weights are proportional to the difference between uniformly distributed ``fitness'' values assigned to the nodes. It is shown analytically and experimentally that the strength (i.e. the weighted node degree) density of such a network model can be well approximated by a power law with γ≈ 0.35. Possible implications for neuronal networks topology and dynamics are also discussed.
Create a lesson
Related papers
Glauber dynamics of the Sherrington--Kirkpatrick spin-glass model: a dynamical cavity method
Grzegorz Szamel
Generative diffusion in long-range correlated landscapes: Speciation cascade and the foam structure of the score
Jerome Garnier-Brun, Giulio Biroli, Marc Mézard et al.
A Clifford-index approach connecting boundary Dirac fermions to sigma-model topological terms in the periodic table of topological insulators and superconductors
Y. X. Zhao
Exact solution of the dynamic pseudogap model: Fock-space formalism for Gaussian fluctuations
Michael V. Sadovskii
Covariance-dependent participation scaling of Comtet disorder on open hyperbolic lattices
Wei Li, Yong Cheng
Informative Fisher Modes in Neural Networks as a Result of Explicit Symmetry Breaking
Ibrahim Talha Ersoy, Björn Ladewig, Karoline Wiesner