• DocumentCode
    3573611
  • Title

    Mining Association Rules in Scale-Free Networks

  • Author

    Gao, Li ; Dai, Shang-Ping ; Zhu, Chang-Wu

  • Author_Institution
    Hua Zhong Normal Univ., Wuhan
  • Volume
    2
  • fYear
    2007
  • Firstpage
    777
  • Lastpage
    780
  • Abstract
    For the characteristic of scale-free networks, containing a few nodes that have a very high degree and many with low degree,the high connectivity nodes play an important role of hubs in communication and networking. This characteristic can be exploited with designing efficient search algorithms. This paper proposes an algorithm to change each new node connecting to the network based on its high-degree-probability for equal-degree-probability ,after having constituted initial model by choosing high-degree-probability nodes. We use an association rule search strategy that utilizes high degree nodes in scale-free networks and costs scaling with the size of the graph. We also demonstrate the utility of these CSCCNU network.. It can improve networks´ robustness.
  • Keywords
    data mining; probability; search problems; association rule search strategy; equal-degree-probability; high-degree-probability; mining association rules; scale-free networks; search algorithms; Algorithm design and analysis; Association rules; Computer science; Cybernetics; Data mining; Electronic mail; IP networks; Intelligent networks; Joining processes; Machine learning; Association rules; Data mining; Genetic algorithm; Hubs; Scale-free networks;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics, 2007 International Conference on
  • Print_ISBN
    978-1-4244-0973-0
  • Electronic_ISBN
    978-1-4244-0973-0
  • Type

    conf

  • DOI
    10.1109/ICMLC.2007.4370248
  • Filename
    4370248