Optimizing K2 trees: A case for validating the maturity of network of practices

  • Authors:

  • Quan Shi;Yanghua Xiao;Nik Bessis;Yiqi Lu;Yaoliang Chen;Richard Hill

  • Affiliations:

  • School of Computer Science and Technology, Nantong University, Nantong, China;School of Computer Science, Fudan University, Shanghai, China;School of Computing and Mathematics, University of Derby, United Kingdom;School of Computer Science, Fudan University, Shanghai, China;School of Computer Science, Fudan University, Shanghai, China;School of Computing and Mathematics, University of Derby, United Kingdom

  • Venue:
  • Computers & Mathematics with Applications
  • Year:
  • 2012

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Abstract

Of late there has been considerable interest in the efficient and effective storage of large-scale network graphs, such as those within the domains of social networks, web and virtual communities. The representation of these data graphs is a complex and challenging task and arises as a result of the inherent structural and dynamic properties of a community network, whereby naturally occurring churn can severely affect the ability to optimize the network structure. Since the organization of the network will change over time, we consider how an established method for storing large data graphs (K^2 tree) can be augmented and then utilized as an indicator of the relative maturity of a community network. Within this context, we present an algorithm and a series of experimental results upon both real and simulated networks, illustrating that the compression effectiveness reduces as the community network structure becomes more dynamic. It is for this reason we highlight a notable opportunity to explore the relevance between the K^2 tree optimization factor with the maturity level of the network community concerned.