Journal
GENES & GENETIC SYSTEMS
Volume 84, Issue 5, Pages 385-395Publisher
GENETICS SOC JAPAN
DOI: 10.1266/ggs.84.385
Keywords
real-world complex networks; network motifs; subgraph isomorphism; induced and non-induced subgraphs; subgraph sampling; pattern growth approach
Funding
- Iran National Science Foundation
Ask authors/readers for more resources
In recent years, interest has been growing in the study of complex networks. Since Erdos and Renyi (1960) proposed their random graph model about 50 years ago, many researchers have investigated and shaped this field. Many indicators have been proposed to assess the global features of networks. Recently, an active research area has developed in studying local features named motifs as the building blocks of networks. Unfortunately, network motif discovery is a computationally hard problem and finding rather large motifs (larger than 8 nodes) by means of current algorithms is impractical as it demands too much computational effort. In this paper, we present a new algorithm (MODA) that incorporates techniques such as a pattern growth approach for extracting larger motifs efficiently. We have tested our algorithm and found it able to identify larger motifs with more than 8 nodes more efficiently than most of the current state-of-the-art motif discovery algorithms. While most of the algorithms rely on induced subgraphs as motifs of the networks, MODA is able to extract both induced and non-induced subgraphs simultaneously. The MODA source code is freely available at: http://LBB.utac.ir/Downloacl/LBBsoft/MODA/
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available