Abstract. Statistical systems on random networks can be formulated in terms of partition functions expressed with integrals by regarding Feynman diagrams as random networks. We consider the cases of random networks with bounded but generic degrees of vertices, and show that the free energies can be exactly evaluated in the thermodynamic limit by the Laplace method, and that the exact expressions can in principle be obtained by solving polynomial equations for mean fields. As demonstrations, we apply our method to the ferromagnetic Ising models on random networks. The free energy of the ferromagnetic Ising model on random networks with trivalent vertices is shown to exactly reproduce that of the ferromagnetic Ising model on the Bethe lattice...
The presence of a random magnetic field in ferromagnetic systems leads, in the broken phase, to an a...
We present a detailed study of the phase diagram of the Ising model in random graphs with arbitrary ...
22 pages, LateX, no figureUsing a maximum entropy principle to assign a statistical weight to any gr...
We study a ferromagnetic Ising model on random graphs with a power-law degree distribution and compu...
In the past decades complex networks and their behavior have attracted much attention. In the real w...
The authors study the problem of bipartitioning a random graph of fixed finite valence using a mean-...
We develop a recently-proposed mapping of the two-dimensional Ising model with random exchange (RBIM...
We derive the analytical expression for the first finite-size correction to the average free energy ...
We introduce a statistical system on random networks of trivalent vertices for the purpose of studyi...
The ferromagnetic Ising model is a paradigmatic model of statistical physics used to study phase tr...
We consider spin models on complex networks frequently used to model social and technological system...
We establish an explicit formula for the limiting free energy density (log-partition function divide...
We investigate the potential for exact computations in the statistical mechanics of disordered syste...
This paper develops results for the next nearest neighbour Ising model on random graphs. Besides bei...
The main goal of the paper is to prove central limit theorems for the magnetization rescaled by vN f...
The presence of a random magnetic field in ferromagnetic systems leads, in the broken phase, to an a...
We present a detailed study of the phase diagram of the Ising model in random graphs with arbitrary ...
22 pages, LateX, no figureUsing a maximum entropy principle to assign a statistical weight to any gr...
We study a ferromagnetic Ising model on random graphs with a power-law degree distribution and compu...
In the past decades complex networks and their behavior have attracted much attention. In the real w...
The authors study the problem of bipartitioning a random graph of fixed finite valence using a mean-...
We develop a recently-proposed mapping of the two-dimensional Ising model with random exchange (RBIM...
We derive the analytical expression for the first finite-size correction to the average free energy ...
We introduce a statistical system on random networks of trivalent vertices for the purpose of studyi...
The ferromagnetic Ising model is a paradigmatic model of statistical physics used to study phase tr...
We consider spin models on complex networks frequently used to model social and technological system...
We establish an explicit formula for the limiting free energy density (log-partition function divide...
We investigate the potential for exact computations in the statistical mechanics of disordered syste...
This paper develops results for the next nearest neighbour Ising model on random graphs. Besides bei...
The main goal of the paper is to prove central limit theorems for the magnetization rescaled by vN f...
The presence of a random magnetic field in ferromagnetic systems leads, in the broken phase, to an a...
We present a detailed study of the phase diagram of the Ising model in random graphs with arbitrary ...
22 pages, LateX, no figureUsing a maximum entropy principle to assign a statistical weight to any gr...