• DocumentCode
    115176
  • Title

    On the strengths of connectivity and robustness in general random intersection graphs

  • Author

    Jun Zhao ; Yagan, Osman ; Gligor, Virgil

  • Author_Institution
    Dept. of ECE, Carnegie Mellon Univ., Pittsburgh, PA, USA
  • fYear
    2014
  • fDate
    15-17 Dec. 2014
  • Firstpage
    3661
  • Lastpage
    3668
  • Abstract
    Random intersection graphs have received much attention for nearly two decades, and currently have a wide range of applications ranging from key predistribution in wireless sensor networks to modeling social networks. In this paper, we investigate the strengths of connectivity and robustness in a general random intersection graph model. Specifically, we establish sharp asymptotic zero-one laws for k-connectivity and k-robustness, as well as the asymptotically exact probability of k-connectivity, for any positive integer k. The k-connectivity property quantifies how resilient is the connectivity of a graph against node or edge failures. On the other hand, k-robustness measures the effectiveness of local diffusion strategies (that do not use global graph topology information) in spreading information over the graph in the presence of misbehaving nodes. In addition to presenting the results under the general random intersection graph model, we consider two special cases of the general model, a binomial random intersection graph and a uniform random intersection graph, which both have numerous applications as well. For these two specialized graphs, our results on asymptotically exact probabilities of k-connectivity and asymptotic zero-one laws for k-robustness are also novel in the literature.
  • Keywords
    graph theory; probability; random processes; wireless sensor networks; asymptotic zero-one laws; asymptotically exact probability; binomial random intersection graph model; connectivity strengths; edge failures; global graph topology information; k-connectivity; k-connectivity property; k-robustness; key predistribution; local diffusion strategies; social network modeling; uniform random intersection graph model; wireless sensor networks; Cryptography; Probability distribution; Random variables; Robustness; Silicon; Topology; Wireless sensor networks; Connectivity; consensus; random graph; random intersection graph; random key graph; robustness;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Decision and Control (CDC), 2014 IEEE 53rd Annual Conference on
  • Conference_Location
    Los Angeles, CA
  • Print_ISBN
    978-1-4799-7746-8
  • Type

    conf

  • DOI
    10.1109/CDC.2014.7039959
  • Filename
    7039959