Genetic Programming for the Automatic Inference of Graph Models for Complex Networks

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@Article{Bailey:2014:ieeeTEC,
  author =       "Alexander Bailey and Mario Ventresca and 
                 Beatrice Ombuki-Berman",
  title =        "Genetic Programming for the Automatic Inference of
                 Graph Models for Complex Networks",
  journal =      "IEEE Transactions on Evolutionary Computation",
  year =         "2014",
  volume =       "18",
  number =       "3",
  pages =        "405--419",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming, complex
                 networks, Evolutionary Computation",
  ISSN =         "1089-778X",
  DOI =          "doi:10.1109/TEVC.2013.2281452",
  size =         "15 pages",
  abstract =     "Complex networks are becoming an integral tool for our
                 understanding of an enormous variety of natural and
                 artificial systems. A number of human-designed network
                 generation procedures have been proposed that
                 reasonably model specific real-life phenomena in
                 structure and dynamics. Consequently, breakthroughs in
                 genetics, medicine, epidemiology, neuroscience,
                 telecommunications and the social sciences have
                 recently resulted. A graph model is an algorithm
                 capable of constructing arbitrarily sized networks,
                 whose end structure will exhibit certain statistical
                 and structural properties. The process of deriving an
                 accurate graph model is very time intensive and
                 challenging and may only yield highly accurate models
                 for very specific phenomena. An automated approach
                 based on Genetic Programming was recently proposed by
                 the authors. However, this initial system suffered from
                 a number of drawbacks, including an under-emphasis on
                 creating hub vertices, the requirement of user
                 intervention to determine objective weights and the
                 arbitrary approach to selecting the most representative
                 model from a population of candidate models. In this
                 paper we propose solutions to these problems and show
                 experimentally that the new system represents a
                 significant improvement and is very capable of
                 reproducing existing common graph models from even a
                 single small initial network.",
  notes =        "also known as \cite{6595618}",
}

Genetic Programming entries for Alexander Bailey Mario Ventresca Beatrice Ombuki-Berman

Citations