A meta-analysis of centrality measures for comparing and generating complex network models

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Kyle Robert Harrison and Mario Ventresca and 
                 Beatrice M. Ombuki-Berman",
  title =        "A meta-analysis of centrality measures for comparing
                 and generating complex network models",
  journal =      "Journal of Computational Science",
  year =         "2015",
  ISSN =         "1877-7503",
  DOI =          "doi:10.1016/j.jocs.2015.09.011",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1877750315300259",
  abstract =     "Complex networks are often characterized by their
                 statistical and topological network properties such as
                 degree distribution, average path length, and
                 clustering coefficient. However, many more
                 characteristics can also be considered such as graph
                 similarity, centrality, or flow properties. These
                 properties have been used as feedback for algorithms
                 whose goal is to ascertain plausible network models
                 (also called generators) for a given network. However,
                 a good set of network measures to employ that can be
                 said to sufficiently capture network structure is not
                 yet known. In this paper we provide an investigation
                 into this question through a meta-analysis that
                 quantifies the ability of a subset of measures to
                 appropriately compare model (dis)similarity. The
                 results are used as fitness measures for improving a
                 recently proposed genetic programming (GP) framework
                 that is capable of ascertaining a plausible network
                 model from a single network observation. It is shown
                 that the candidate model evaluation criteria of the GP
                 system to automatically infer existing (man-made)
                 network models, in addition to real-world networks, is
  keywords =     "genetic algorithms, genetic programming, Complex
                 networks, Graph models, Cortical networks,

Genetic Programming entries for Kyle Robert Harrison Mario Ventresca Beatrice Ombuki-Berman