Abstract
A network motif is a recurring subnetwork within a network, and it takes on certain functions in practical biological macromolecule applications. Previous algorithms have focused on the computational efficiency of network motif detection, but some problems in storage space and searching time manifested during earlier studies. The considerable computational and spacial complexity also presents a significant challenge. In this paper, we provide a new approach for motif mining based on compressing the searching space. According to the characteristic of the parity nodes, we cut down the searching space and storage space in real graphs and random graphs, thereby reducing the computational cost of verifying the isomorphism of sub-graphs. We obtain a new network with smaller size after removing parity nodes and the “repeated edges” connected with the parity nodes. Random graph structure and sub-graph searching are based on the Back Tracking Method; all sub-graphs can be searched for by adding edges progressively. Experimental results show that this algorithm has higher speed and better stability than its alternatives.Electronic supplementary materialThe online version of this article (doi:10.1186/s13040-014-0029-x) contains supplementary material, which is available to authorized users.
Highlights
Researchers have discovered that the human genome is a complex network system
Zhang and Lu [19] employed a network stratification strategy to investigate the validity of the current network analysis of conglomerate PPI networks, finding that network stratification may help to resolve many controversies in the current research of systems biology
We evaluate our method on the metabolic pathway of the bacteria E. coli, the transcription network of Yeast, the Sea Urchin network, and an electronic network
Summary
Researchers have discovered that the human genome is a complex network system. With the Human Genome Project (HGP), we step into a post-genome era. An algorithm to reduce the complexity of matching two graphs was proposed by Knossow,Sharma, Mateus and Horaud [14] Another algorithm that optimizes the one-to-many matching problem was introduced by Ogras and Marculescu [15]. Srinivasan,Vural, King and Guda [20] presented a new substitution-based scoring function for identifying discriminative lower denominations that are highly specific to a class. Some of these methods, designed for both directed and undirected graphs, proved to be time-consuming. The aim of this paper is to achieve a method for reducing the searching time storage space required for a motif mining algorithm, while storing all sub-graphs
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