Automatic Inference of Graph Models for Directed Complex Networks using Genetic Programming

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  author =       "Michael Richard Medland and Kyle Robert Harrison and 
                 Beatrice M. Ombuki-Berman",
  title =        "Automatic Inference of Graph Models for Directed
                 Complex Networks using Genetic Programming",
  booktitle =    "Proceedings of 2016 IEEE Congress on Evolutionary
                 Computation (CEC 2016)",
  year =         "2016",
  editor =       "Yew-Soon Ong",
  pages =        "2337--2344",
  address =      "Vancouver",
  month =        "24-29 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-5090-0623-6",
  DOI =          "doi:10.1109/CEC.2016.7744077",
  abstract =     "Complex networks are systems of entities that are
                 interconnected through meaningful relationships,
                 resulting in structures that have statistical
                 complexities not formed by random chance. Many graph
                 model algorithms have been proposed to model the
                 observed behaviours of complex networks. However,
                 constructing such graph models manually is both tedious
                 and problematic. Moreover, many of the models proposed
                 in the literature have been cited as having
                 inaccuracies with respect to the complex networks they
                 represent. Although recent studies have proposed using
                 genetic programming to automate the construction of
                 graph model algorithms, only one such study has
                 considered directed networks. This paper proposes a
                 GP-based inference system that automatically constructs
                 graph models for directed complex networks.
                 Furthermore, the system proposed in this paper
                 facilitates the use of vertex attributes, e.g., age, to
                 incorporate network semantics - something which
                 previous works lack. The GP system was used to
                 reproduce three well-known graph models. Results
                 indicate that the networks generated by the
                 (automatically) constructed models were structurally
                 similar to networks generated by their respective
                 target models.",
  notes =        "WCCI2016",

Genetic Programming entries for Michael Medland Kyle Robert Harrison Beatrice Ombuki-Berman