• DocumentCode
    270473
  • Title

    acc-Motif: Accelerated Network Motif Detection

  • Author

    Meira, Luis A. A. ; Maximo, Vinicius R. ; Fazenda, Álvaro L. ; da Conceicao, Arlindo F.

  • Author_Institution
    Sch. of Technol., Univ. of Campinas (UNICAMP), Campinas, Brazil
  • Volume
    11
  • Issue
    5
  • fYear
    2014
  • fDate
    Sept.-Oct. 1 2014
  • Firstpage
    853
  • Lastpage
    862
  • Abstract
    Network motif algorithms have been a topic of research mainly after the 2002-seminal paper from Milo et al. [1], which provided motifs as a way to uncover the basic building blocks of most networks. Motifs have been mainly applied in Bioinformatics, regarding gene regulation networks. Motif detection is based on induced subgraph counting. This paper proposes an algorithm to count subgraphs of size k + 2 based on the set of induced subgraphs of size k. The general technique was applied to detect 3, 4 and 5-sized motifs in directed graphs. Such algorithms have time complexity O(a(G)m), O(m2) and O(nm2), respectively, where a(G) is the arboricity of G(V, E). The computational experiments in public data sets show that the proposed technique was one order of magnitude faster than Kavosh and FANMOD. When compared to NetMODE, acc-Motif had a slightly improved performance.
  • Keywords
    bioinformatics; computational complexity; genetics; graphs; 3-sized motifs; 4-sized motifs; 5-sized motifs; FANMOD; Kavosh; acc-Motif; accelerated network motif detection; basic building blocks; bioinformatics; gene regulation networks; induced subgraph counting; network motif algorithms; public data sets; subgraph size; time complexity; Acceleration; Bioinformatics; Complex networks; Computational biology; Educational institutions; Time complexity; Network motifs; algorithm analysis; subgraph counting;
  • fLanguage
    English
  • Journal_Title
    Computational Biology and Bioinformatics, IEEE/ACM Transactions on
  • Publisher
    ieee
  • ISSN
    1545-5963
  • Type

    jour

  • DOI
    10.1109/TCBB.2014.2321150
  • Filename
    6808514