DocumentCode
270473
Title
acc-Motif: Accelerated Network Motif Detection
Author
Meira, Luis A. A. ; Maximo, Vinicius R. ; Fazenda, AÌlvaro 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
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