Majority-vote model on random graphs

Abstract

The majority-vote model with noise on random graphs has been studied. Monte Carlo simulations were performed to characterize the order-disorder phase transition appearing in the system. We found that the value of the critical noise parameter is an increasing function of the mean connectivity of the random graph. The critical exponents beta/nu, gamma/nu and 1/nu were calculated for several values of z, and our analysis yielded critical exponents satisfying the hyperscaling relation with effective dimensionality equal to unity.

4 pages, 6 figures

References in corpus (5)

• Emergence of scaling in random networks
• Statistical mechanics of complex networks
• Random Geometric Graphs
• Statistical mechanics of topological phase transitions in networks
• Scale free networks from a Hamiltonian dynamics

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