• DocumentCode
    1305296
  • Title

    Assortative mixing in directed biological networks

  • Author

    Piraveenan, M. ; Prokopenko, M. ; Zomaya, A.

  • Author_Institution
    Sch. of Inf. Technol., Univ. of Sydney, Sydney, NSW, Australia
  • Volume
    9
  • Issue
    1
  • fYear
    2012
  • Firstpage
    66
  • Lastpage
    78
  • Abstract
    We analyze assortative mixing patterns of biological networks which are typically directed. We develop a theoretical background for analyzing mixing patterns in directed networks before applying them to specific biological networks. Two new quantities are introduced, namely the in-assortativity and the out-assortativity, which are shown to be useful in quantifying assortative mixing in directed networks. We also introduce the local (node level) assortativity quantities for in- and out-assortativity. Local assortativity profiles are the distributions of these local quantities over node degrees and can be used to analyze both canonical and real-world directed biological networks. Many biological networks, which have been previously classified as disassortative, are shown to be assortative with respect to these new measures. Finally, we demonstrate the use of local assortativity profiles in analyzing the functionalities of particular nodes and groups of nodes in real-world biological networks.
  • Keywords
    network topology; assortative mixing patterns; biological networks; Bioinformatics; Biology; Book reviews; Computational biology; Equations; Probability distribution; Regulators; Networks; assortativity; biological networks.; graph theory; systems biology; Animals; Bacteria; Food Chain; Gene Regulatory Networks; Humans; Models, Biological; Nerve Net; Systems Biology;
  • fLanguage
    English
  • Journal_Title
    Computational Biology and Bioinformatics, IEEE/ACM Transactions on
  • Publisher
    ieee
  • ISSN
    1545-5963
  • Type

    jour

  • DOI
    10.1109/TCBB.2010.80
  • Filename
    5557854