Symbolic regression of generative network models

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@Article{Menezes:2014:SR,
  author =       "Telmo Menezes and Camille Roth",
  title =        "Symbolic regression of generative network models",
  journal =      "Scientific Reports",
  year =         "2014",
  volume =       "4",
  number =       "6284",
  month =        "5 " # sep,
  keywords =     "genetic algorithms, genetic programming, Machine
                 learning. Applied mathematics, Scientific data,
                 Software",
  ISSN =         "2045-2322",
  URL =          "http://www.telmomenezes.com/2014/09/using-evolutionary-computation-to-explain-network-growth/",
  URL =          "http://www.nature.com/srep/2014/140905/srep06284/full/srep06284.html",
  DOI =          "doi:10.1038/srep06284",
  size =         "7 pages",
  abstract =     "Networks are a powerful abstraction with applicability
                 to a variety of scientific fields. Models explaining
                 their morphology and growth processes permit a wide
                 range of phenomena to be more systematically analysed
                 and understood. At the same time, creating such models
                 is often challenging and requires insights that may be
                 counter-intuitive. Yet there currently exists no
                 general method to arrive at better models. We have
                 developed an approach to automatically detect realistic
                 decentralised network growth models from empirical
                 data, employing a machine learning technique inspired
                 by natural selection and defining a unified formalism
                 to describe such models as computer programs. As the
                 proposed method is completely general and does not
                 assume any pre-existing models, it can be applied out
                 of the box to any given network. To validate our
                 approach empirically, we systematically rediscover
                 pre-defined growth laws underlying several canonical
                 network generation models and credible laws for diverse
                 real-world networks. We were able to find programs that
                 are simple enough to lead to an actual understanding of
                 the mechanisms proposed, namely for a simple brain and
                 a social network.",
  notes =        "https://groups.yahoo.com/neo/groups/genetic_programming/conversations/messages/6512
                 open source tool that implements the methodology
                 describe in the paper:
                 https://github.com/telmomenezes/synthetic",
}

Genetic Programming entries for Telmo Menezes Camille Roth

Citations