• Login
    View Item 
    •   Repository Home
    • Brock Theses
    • Masters Theses
    • M.Sc. Computer Science
    • View Item
    •   Repository Home
    • Brock Theses
    • Masters Theses
    • M.Sc. Computer Science
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Automatic Inference of Graph Models for Complex Networks with Genetic Programming

    Thumbnail
    View/Open
    Brock_Bailey_Alexander_2013.pdf (4.210Mb)
    Date
    2013-07-26
    Author
    Bailey, Alexander
    Metadata
    Show full item record
    Abstract
    Complex networks can arise naturally and spontaneously from all things that act as a part of a larger system. From the patterns of socialization between people to the way biological systems organize themselves, complex networks are ubiquitous, but are currently poorly understood. A number of algorithms, designed by humans, have been proposed to describe the organizational behaviour of real-world networks. Consequently, breakthroughs in genetics, medicine, epidemiology, neuroscience, telecommunications and the social sciences have recently resulted. The algorithms, called graph models, represent significant human effort. Deriving accurate graph models is non-trivial, time-intensive, challenging and may only yield useful results for very specific phenomena. An automated approach can greatly reduce the human effort required and if effective, provide a valuable tool for understanding the large decentralized systems of interrelated things around us. To the best of the author's knowledge this thesis proposes the first method for the automatic inference of graph models for complex networks with varied properties, with and without community structure. Furthermore, to the best of the author's knowledge it is the first application of genetic programming for the automatic inference of graph models. The system and methodology was tested against benchmark data, and was shown to be capable of reproducing close approximations to well-known algorithms designed by humans. Furthermore, when used to infer a model for real biological data the resulting model was more representative than models currently used in the literature.
    URI
    http://hdl.handle.net/10464/4719
    Collections
    • M.Sc. Computer Science

    Brock University | Copyright © 2006-2015 
    Contact Us | Send Feedback
     

     

    Browse

    All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    LoginRegister

    Statistics

    View Usage Statistics

    Brock University | Copyright © 2006-2015 
    Contact Us | Send Feedback