Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Complete Mining of Frequent Patterns from Graphs: Mining Graph Data
Machine Learning
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Computing Frequent Graph Patterns from Semistructured Data
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
The predictive toxicology evaluation challenge
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
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
APPT '09 Proceedings of the 8th International Symposium on Advanced Parallel Processing Technologies
NODAR: mining globally distributed substructures from a single labeled graph
Journal of Intelligent Information Systems
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Network motifs, patterns of local interconnections with potential functional properties, are important for the analysis of biological networks. To analyse motifs in networks the first step is to find patterns of interest. This paper presents 1) three different concepts for the determination of pattern frequency and 2) an algorithm to compute these frequencies. The different concepts of pattern frequency depend on the reuse of network elements. The presented algorithm finds all or highly frequent patterns under consideration of these concepts. The utility of this method is demonstrated by applying it to biological data.