{
  "id": "1702.06822",
  "version": "v1",
  "published": "2017-02-22T14:53:26.000Z",
  "updated": "2017-02-22T14:53:26.000Z",
  "title": "Variational approximations for stochastic dynamics on graphs",
  "authors": [
    "Alessandro Pelizzola",
    "Marco Pretti"
  ],
  "comment": "28 pages, 5 figures",
  "categories": [
    "cond-mat.stat-mech"
  ],
  "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2017-02-22T14:53:26.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "stochastic dynamics",
      "variational approximations",
      "monte carlo simulations",
      "random graphs",
      "cluster-variational approach"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 28,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}