Frequent Graph Patterns
Graph database mining
There are three key concepts in mining graph databases: (i) labeled graph, (ii) subgraph isomorphism, and (iii) graph support value. Based on the concepts, the problem of frequent subgraph mining could be defined in the following discussion.
Alabeled graphG is a quadrupleG = (V, E, Σ, λ) where V is a set of vertices or nodes and E ⊆ V × V is a set of undirected edges. Σ is a set of (disjoint) vertex and edge labels, and λ: V ∪ E → Σ is a function that assigns labels to vertices and edges. Typically a total ordering is defined on the labels in Σ.
With the previous definition, a graph database is a set of labeled graphs.
- 2.Huan J, Prins J, Wang W, Carter C, Dokholyan NV. Coordinated evolution of protein sequences and structures with structure entropy. Technical Reports Computer Science Department; 2006.Google Scholar
- 3.Huan J, Wang W, Bandyopadhyay D, Snoeyink J, Prins J, Tropsha A. Mining protein family specific residue packing patterns from protein structure graphs. In: Proceedings of the 8th Annual International Conference on Research in Computational Molecular Biology; 2004. p. 308–15.Google Scholar
- 4.Huan J, Wang W, Prins J. Efficient mining of frequent subgraph in the presence of isomorphism. In: Proceedings of the 3rd IEEE International Conference on Data Mining; 2003. p. 549–52.Google Scholar
- 5.Huan J, Wang W, Prins J, Yang J. SPIN: mining maximal frequent subgraphs from graph databases. In: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2004. p. 581–6.Google Scholar
- 7.Kuramochi M., Karypis G. Frequent subgraph discovery. In: Proceedings of the 1st IEEE International Conference on Data Mining; 2001. p. 313–20.Google Scholar
- 8.Nijssen S, Kok J. A quickstart in frequent structure mining can make a difference. In: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2004.p. 647–52.Google Scholar
- 9.Smalter A, Huan J, Lushington G. Structure-based pattern mining for chemical compound classification. In: Proceedings of the 6th Asia Pacific Bioinformatics Conference; 2008. p. 39–48.Google Scholar
- 10.Vanetik N, Gudes E. Mining frequent labeled and partially labeled graph patterns. In: Proceedings of the 20th International Conference on Data Engineering; 2004. p. 91–102.Google Scholar
- 11.Yan X, Han J. gSpan: graph-based substructure pattern mining. In: Proceedings of the 2nd IEEE International Conference on Data Mining; 2002. p. 721–4.Google Scholar