Evolutionary Modeling of a Blog Network

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  title =        "Evolutionary Modeling of a Blog Network",
  author =       "Telmo Menezes",
  pages =        "908--915",
  booktitle =    "Proceedings of the 2011 IEEE Congress on Evolutionary
  year =         "2011",
  editor =       "Alice E. Smith",
  month =        "5-8 " # jun,
  address =      "New Orleans, USA",
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  ISBN =         "0-7803-8515-2",
  keywords =     "genetic algorithms, genetic programming,
                 Classification, clustering, data analysis and data
                 mining, Coevolution and collective behaviour",
  DOI =          "doi:10.1109/CEC.2011.5949715",
  abstract =     "A common approach to produce theory to explain the
                 genesis and dynamics of complex networks is to create
                 multi-agent simulations that output networks with
                 similar characteristics to the ones derived from real
                 data. For example, a well know explanation for the
                 power law degree distributions found in blog (and
                 other) networks is the agent-level endogenous mechanism
                 of preferential attachment. However, once simplifying
                 assumptions are dropped, finding lower level behaviours
                 that explain global network features can become
                 difficult. One case, explored in this paper, is that of
                 modelling a blog network generated by human agents with
                 heterogeneous behaviours and a priori diversity. We
                 propose an approach based on an hybrid strategy,
                 combining a generic behavioural template created by a
                 human designer with a set of programs evolved using
                 genetic programming. We present experimental results
                 that illustrate how this approach can be successfully
                 used to discover a set of non-trivial agent-level
                 behaviours that generate a network that fits observed
                 data. We then use the model to make successful testable
                 predictions about the real data. We analyse the
                 diversity of behaviours found in the evolved model by
                 clustering the agents according to the execution paths
                 their programs take during the simulation. We show that
                 these clusters map to different behaviours, giving
                 credence to the need for exogenous, in addition to the
                 more conventional endogenous explanations, for the
                 dynamics of blog networks.",
  notes =        "CEC2011 sponsored by the IEEE Computational
                 Intelligence Society, and previously sponsored by the
                 EPS and the IET.",

Genetic Programming entries for Telmo Menezes