TY  - JOUR
A1  - Khakabimamaghani, Sahand
A1  - Sharafuddin, Iman
A1  - Dichter, Norbert
A1  - Koch, Ina
A1  - Masoudi-Nejad, Ali
T1  - QuateXelero : an accelerated exact network motif detection algorithm
T2  - PLoS One
N2  - 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.
Y1  - 2013
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/31134
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-311344
SN  - 1932-6203
N1  - Copyright: © 2013 Khakabimamaghani et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
VL  - 8
IS  - (7):e68073
PB  - PLoS
CY  - Lawrence, Kan.
ER  -