Variational approximations for stochastic dynamics on graphs

Abstract

We investigate different mean-field-like approximations for stochastic dynamics on graphs, within the framework of a cluster-variational approach. In analogy with its equilibrium counterpart, this approach allows one to give a unified view of various (previously known) approximation schemes, and suggests quite a systematic way to improve the level of accuracy. We compare the different approximations with Monte Carlo simulations on a reversible (susceptible-infected-susceptible) discrete-time epidemic-spreading model on random graphs.

29 pages, 5 figures. Minor revisions. IOP-styled

References in corpus (3)

• Statistical physics of social dynamics
• Unification of theoretical approaches for epidemic spreading on complex networks
• Cluster approximations for probabilistic systems: a new perspective of epidemiological modelling

Cited by in corpus (4)

• Cluster approximations for the TASEP: stationary state and dynamical transition
• Matrix Product Belief Propagation for reweighted stochastic dynamics over graphs
• The matrix product approximation for the dynamic cavity method
• Coordination problems on networks revisited: statics and dynamics