title: QuateXelero: an accelerated exact network motif detection algorithm.
abstract: Finding motifs in biological, social, technological, and other types of networks has become a widespread method to gain more knowledge about these networks' structure and function. However, this task is very computationally demanding, because it is highly associated with the graph isomorphism which is an NP problem (not known to belong to P or NP-complete subsets yet). Accordingly, this research is endeavoring to decrease the need to call NAUTY isomorphism detection method, which is the most time-consuming step in many existing algorithms. The work provides an extremely fast motif detection algorithm called QuateXelero, which has a Quaternary Tree data structure in the heart. The proposed algorithm is based on the well-known ESU (FANMOD) motif detection algorithm. The results of experiments on some standard model networks approve the overal superiority of the proposed algorithm, namely QuateXelero, compared with two of the fastest existing algorithms, G-Tries and Kavosh. QuateXelero is especially fastest in constructing the central data structure of the algorithm from scratch based on the input network.
authors.affiliation: Laboratory of Systems Biology and Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
authors.forename: Sahand
authors.initials: S
authors.lastname: Khakabimamaghani
authors.forename: Iman
authors.initials: I
authors.lastname: Sharafuddin
authors.forename: Norbert
authors.initials: N
authors.lastname: Dichter
authors.forename: Ina
authors.initials: I
authors.lastname: Koch
authors.forename: Ali
authors.initials: A
authors.lastname: Masoudi-Nejad
mesh_terms: D000465:Algorithms; D002965:Classification / Q000379:methods; D003198:Computer Simulation; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated / Q000379:methods*; D013997:Time Factors