Abstract
Recently, due to its wide applications, (similar) subgraph search has attracted a lot of attentions from database and data mining community, such as [13,18,19,5]. In [8], Ke et al. first proposed correlation sub-graph search problem (CGSearch for short) to capture the underlying dependency between sub-graphs in a graph database, that is CGS algorithm. However, CGS algorithm requires the specification of a minimum correlation threshold θ to perform computation. In practice, it may not be trivial for users to provide an appropriate threshold θ, since different graph databases typically have different characteristics. Therefore, we propose an alternative mining task: top -K c orrelation sub- g raph search(TOP-CGSearh for short). The new problem itself does not require setting a correlation threshold, which leads the previous proposed CGS algorithm inefficient if we apply it directly to TOP-CGSearch problem. To conduct TOP-CGSearch efficiently, we develop a p attern- g rowth algorithm (that is PG-search algorithm) and utilize graph indexing methods to speed up the mining task. Extensive experiment results evaluate the efficiency of our methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: VLDB (1994)
Brin, S., Motwani, R., Silverstein, C.: Beyond market baskets: Generalizing association rules to correlations. In: SIGMOD (1997)
Cai, D., Shao, Z., He, X., Yan, X., Han, J.: Community mining from multi-relational networks. In: Jorge, A.M., Torgo, L., Brazdil, P.B., Camacho, R., Gama, J. (eds.) PKDD 2005. LNCS, vol. 3721, pp. 445–452. Springer, Heidelberg (2005)
Fortin, S.: The graph isomorphism problem. Department of Computing Science, University of Alberta (1996)
He, H., Singh, A.K.: Closure-tree: An index structure for graph queries. In: ICDE (2006)
Ilyas, I.F., Markl, V., Haas, P.J., Brown, P., Aboulnaga, A.: Cords: Automatic discovery of correlations and soft functional dependencies. In: SIGMOD (2004)
Inokuchi, A., Washio, T., Motoda, H.: An apriori-based algorithm for mining frequent substructures from graph data. In: Zighed, D.A., Komorowski, J., Żytkow, J.M. (eds.) PKDD 2000. LNCS, vol. 1910, pp. 13–23. Springer, Heidelberg (2000)
Ke, Y., Cheng, J., Ng, W.: Correlation search in graph databases. In: SIGKDD (2007)
Kuramochi, M., Karypis, G.: Frequent subgraph discovery. In: ICDM (2001)
Omiecinski, E.: Alternative interest measures for mining associations in databases. IEEE TKDE 15(1) (2003)
Pan, J.-Y., Yang, H.-J., Faloutsos, C., Duygulu, P.: Automatic multimedia cross-modal correlation discovery. In: KDD (2004)
Petrakis, E.G.M., Faloutsos, C.: Similarity searching in medical image databases. IEEE Transactions on Knowledge and Data Enginnering 9(3) (1997)
Shasha, D., Wang, J.T.-L., Giugno, R.: Algorithmics and applications of tree and graph searching. In: PODS (2002)
Willett, P.: Chemical similarity searching. J. Chem. Inf. Comput. Sci. 38(6) (1998)
Xiong, H., Brodie, M., Ma, S.: Top-cop: Mining top-k strongly correlated pairs in large databases. In: Perner, P. (ed.) ICDM 2006. LNCS, vol. 4065. Springer, Heidelberg (2006)
Xiong, H., Shekhar, S., Tan, P.-N., Kumar, V.: Exploiting a support-based upper bound of pearson’s correlation coefficient for efficiently identifying strongly correlated pairs. In: KDD (2004)
Yan, X., Han, J.: gspan: Graph-based substructure pattern mining. In: ICDM (2002)
Yan, X., Yu, P.S., Han, J.: Graph indexing: A frequent structure-based approach. In: SIGMOD (2004)
Yan, X., Yu, P.S., Han, J.: Substructure similarity search in graph databases, pp. 766– 777 (2005)
Zou, L., Chen, L., Yu, J.X., Lu, Y.: A novel spectral coding in a large graph database. In: EDBT (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zou, L., Chen, L., Lu, Y. (2009). Top-K Correlation Sub-graph Search in Graph Databases. In: Zhou, X., Yokota, H., Deng, K., Liu, Q. (eds) Database Systems for Advanced Applications. DASFAA 2009. Lecture Notes in Computer Science, vol 5463. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00887-0_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-00887-0_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-00886-3
Online ISBN: 978-3-642-00887-0
eBook Packages: Computer ScienceComputer Science (R0)