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

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@Article{Harrison:2015:JCS,

• 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 improved.",
• keywords = "genetic algorithms, genetic programming, Complex networks, Graph models, Cortical networks, Meta-analysis",

}

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

Citations