Abstract
This paper proposes an efficient method for indexing and mining graph database. Most existing approaches are based on frequent sub-structures such as edges, paths, or subgraphs. However, as the size of graphs increases, such index structure grows drastically in size for avoiding performance degradation. This yields a requirement for constructing a more compact index structure and introducing more informative indexing items into this index to increase its pruning power. In this paper, we demonstrate that degree information can help solve this problem. Based on this idea, we propose a new index structure (D-index) which uses the subgraph and its degree information as the indexing item. Our empirical study shows that D-index achieves remarkable improvement in performance over the state-of-the-art approach.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Shasha, D., Wang, J.T.L., Giugno, R.: Algorithmics and applications of tree and graph searching. In: Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems 2002, June 03-05, Madison, Wisconsin (2002)
Yan, X., Yu, P.S., Han, J.: Graph indexing: A frequent structure based approach. In: Proceedings of the 2004 ACM SIGMOD international conference on Management of data 2004, Paris, France, June 13 - 18 (2004)
X.Yan and J.Hangspan: Graph-based substructure pattern mining. Second IEEE International Conference on Data Mining (ICDM’02), 2002.
Goethals, B., Hoekx, E., Van den Bussche, J.: Mining tree queries in a graph. In: Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining 2005, Chicago, Illinois, USA, August 21 - 24 (2005)
Giugno, R., Shasha, D.: Graphgrep: A fast and universal method for querying graphs. In: 16th International Conference on Pattern Recognition (ICPR 2002), vol. 2 (2002)
Kuramochi, M., Karypis, G.: Frequent subgraph discovery. In: First IEEE International Conference on Data Mining, ICDM (2001)
Wang, J., Zeng, Z., Zhou, L.: CLAN: An Algorithm for Mining Closed Cliques from Large Dense Graph Databases. In: Proceedings of the 22nd International Conference on Data Engineering (ICDE 2006), April 03 - 07 (2006)
Huan, J., Wang, W., Prins, J., Yang, J.: SPIN: Mining Maximal Frequent Subgraphs from Graph Databases. In: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining 2004, Seattle, WA, USA, August 22 - 25 (2004)
Yan, X., Zhu, F., Han, J., Yu, P.S.: Searching Substructures with Superimposed Distance. In: 22nd International Conference on Data Engineering, ICDE 2006 (2006)
Yan, X., Yu, P.S., Han, J.: Substructure Similarity Search in Graph Databases. In: Proceedings of the 2005 ACM SIGMOD international conference on Management of data 2005, Baltimore, Maryland, June 14 - 16 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Shang, H., Jin, X. (2006). Indexing and Mining of Graph Database Based on Interconnected Subgraph. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_118
Download citation
DOI: https://doi.org/10.1007/11875581_118
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45485-4
Online ISBN: 978-3-540-45487-8
eBook Packages: Computer ScienceComputer Science (R0)