Statistical mechanics of coevolving spin system

Abstract

We propose a statistical mechanics approach to a coevolving spin system with an adaptive network of interactions. The dynamics of node states and network connections is driven by both spin configuration and network topology. We consider a Hamiltonian that merges the classical Ising model and the statistical theory of correlated random networks. As a result, we obtain rich phase diagrams with different phase transitions both in the state of nodes and in the graph topology. We argue that the coupling between the spin dynamics and the structure of the network is crucial in understanding the complex behavior of real-world systems and omitting one of the approaches renders the description incomplete.

References in corpus (3)

• Generic Absorbing Transition in Coevolution Dynamics
• Magnetization reversal in spin patterns with complex geometry
• Coevolving complex networks in the model of social interactions

Cited by in corpus (8)

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• Emergence of complex structures from nonlinear interactions and noise in coevolving networks
• Threshold cascade dynamics on coevolving networks
• Spontaneous symmetry breaking of active phase in coevolving nonlinear voter model
• The threshold model with anticonformity under random sequential updating
• Coevolutionary Dynamics of Group Interactions: Coevolving Nonlinear Voter Models
• New Coevolution Dynamic as an Optimization Strategy in Group Problem Solving
• Aging in coevolving voter models