Isomorph-free exhaustive generation
Journal of Algorithms
Combinatorial algorithms: generation, enumeration, and search
ACM SIGACT News
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
MAVisto: a tool for the exploration of network motifs
Bioinformatics
NeMoFinder: dissecting genome-wide protein-protein interactions with meso-scale network motifs
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
FANMOD: a tool for fast network motif detection
Bioinformatics
Efficient Detection of Network Motifs
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Note: Symmetry in complex networks
Discrete Applied Mathematics
Network motif discovery using subgraph enumeration and symmetry-breaking
RECOMB'07 Proceedings of the 11th annual international conference on Research in computational molecular biology
Combinatorial algorithms for listing paths in minimal change order
CAAN'07 Proceedings of the 4th conference on Combinatorial and algorithmic aspects of networking
Efficient Counting of Network Motifs
ICDCSW '10 Proceedings of the 2010 IEEE 30th International Conference on Distributed Computing Systems Workshops
Mathematics of Bioinformatics: Theory, Methods and Applications
Mathematics of Bioinformatics: Theory, Methods and Applications
A faster algorithm for detecting network motifs
WABI'05 Proceedings of the 5th International conference on Algorithms in Bioinformatics
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Discovering network motifs could provide a significant insight into systems biology. Interestingly, many biological networks have been found to have a high degree of symmetry (automorphism), which is inherent in biological network topologies. The symmetry due to the large number of basic symmetric subgraphs (BSSs) causes a certain redundant calculation in discovering network motifs. Therefore, we compress all basic symmetric subgraphs before extracting compressed subgraphs and propose an efficient decompression algorithm to decompress all compressed subgraphs without loss of any information. In contrast to previous approaches, the novel Symmetry Compression method for Motif Detection, named as SCMD, eliminates most redundant calculations caused by widespread symmetry of biological networks. We use SCMD to improve three notable exact algorithms and two efficient sampling algorithms. Results of all exact algorithms with SCMD are the same as those of the original algorithms, since SCMD is a lossless method. The sampling results show that the use of SCMD almost does not affect the quality of sampling results. For highly symmetric networks, we find that SCMD used in both exact and sampling algorithms can help get a remarkable speedup. Furthermore, SCMD enables us to find larger motifs in biological networks with notable symmetry than previously possible.