• DocumentCode
    3432472
  • Title

    Discovering community membership in biological networks with node topology potential

  • Author

    Xiao, Liping ; Wang, Shuliang ; Li, Jingjing

  • Author_Institution
    Institute of Chinese Electrical, System Engineering, Beijing 100840, China
  • fYear
    2012
  • fDate
    11-13 Aug. 2012
  • Firstpage
    541
  • Lastpage
    546
  • Abstract
    In this paper, a novel approach is proposed to discover community membership in complex networks with node topology potential, along with the experiment on complex biological networks. The concept of physical field is brought into networks. Nodes will have a certain topology potential since they can affect and be affected by the others nearby. And this topology potential is an index to measure the interaction among nodes. Based on the distributing feature of node topology potential, the community memberships are uncovered. A biological network is finally experimented to show the effectiveness of the proposed algorithm.
  • Keywords
    Artificial neural networks; Communications technology; Communities; Robustness; Shape; community membership; complex biological networks; data field; node topology potential;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Granular Computing (GrC), 2012 IEEE International Conference on
  • Conference_Location
    Hangzhou, China
  • Print_ISBN
    978-1-4673-2310-9
  • Type

    conf

  • DOI
    10.1109/GrC.2012.6468676
  • Filename
    6468676