Abstract
Frequent subgraph mining has been an important research problem in the literature. However, the huge number of discovered frequent subgraphs becomes the bottleneck for exploring and understanding the generated patterns. In this paper, we propose to summarize frequent subgraphs with an independence probabilistic model, with the goal to restore the frequent subgraphs and their frequencies accurately from a compact summarization model. To achieve a good summarization quality, our summarization framework allows users to specify an error tolerance σ, and our algorithms will discover k summarization templates in a top-down fashion and keep the frequency restoration error within σ. Experiments on real graph datasets show that our summarization framework can effectively control the frequency restoration error within 10% with a concise summarization model.
This is a preview of subscription content, log in via an institution to check access.
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
Borgelt, C., Berthold, M.R.: Mining molecular fragments: Finding relevant substructures of molecules. In: ICDM, p. 51 (2002)
Bringmann, B., Nijssen, S.: What is frequent in a single graph? In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS (LNAI), vol. 5012, pp. 858–863. Springer, Heidelberg (2008)
Chen, C., Lin, C.X., Yan, X., Han, J.: On effective presentation of graph patterns: a structural representative approach. In: CIKM, pp. 299–308 (2008)
Deshpande, M., Kuramochi, M., Wale, N., Karypis, G.: Frequent substructure-based approaches for classifying chemical compounds. IEEE Trans. on Knowl. and Data Eng. 17(8), 1036–1050 (2005)
Fiedler, M., Borgelt, C.: Subgraph support in a single large graph. In: ICDM Workshops, pp. 399–404 (2007)
Hasan, M.A., Chaoji, V., Salem, S., Besson, J., Zaki, M.J.: Origami: Mining representative orthogonal graph patterns. In: ICDM, pp. 153–162 (2007)
Hasan, M.A., Zaki, M.J.: Output space sampling for graph patterns. Proc. VLDB Endow. 2(1), 730–741 (2009)
Huan, J., Wang, W., Prins, J.: Efficient mining of frequent subgraphs in the presence of isomorphism. In: ICDM, p. 549 (2003)
Huan, J., Wang, W., Prins, J., Yang, J.: Spin: mining maximal frequent subgraphs from graph databases. In: KDD, pp. 581–586 (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 (LNAI), vol. 1910, pp. 13–23. Springer, Heidelberg (2000)
Jin, R., Abu-Ata, M., Xiang, Y., Ruan, N.: Effective and efficient itemset pattern summarization: Regression-based approaches. In: KDD, pp. 399–407 (2008)
Kuramochi, M., Karypis, G.: Frequent subgraph discovery. In: ICDM, pp. 313–320 (2001)
Kuramochi, M., Karypis, G.: Finding frequent patterns in a large sparse graph. Data Min. Knowl. Discov. 11(3), 243–271 (2005)
Li, S., Zhang, S., Yang, J.: DESSIN: Mining dense subgraph patterns in a single graph. In: Gertz, M., Ludäscher, B. (eds.) SSDBM 2010. LNCS, vol. 6187, pp. 178–195. Springer, Heidelberg (2010)
Liu, Y., Li, J., Gao, H.: Summarizing graph patterns. In: ICDE, pp. 903–912 (2008)
Navlakha, S., Rastogi, R., Shrivastava, N.: Graph summarization with bounded error. In: SIGMOD, pp. 419–432 (2008)
Nijssen, S., Kok, J.N.: A quickstart in frequent structure mining can make a difference. In: KDD, pp. 647–652 (2004)
Freund, R.J., Wilson, W.J., Sa, P.: Regression Analysis: Statistical Modeling of a Response Variable, 2nd edn. Academic Press (2006)
Thomas, L.T., Valluri, S.R., Karlapalem, K.: Margin: Maximal frequent subgraph mining. In: ICDM, pp. 1097–1101 (2006)
Tian, Y., Hankins, R.A., Patel, J.M.: Efficient aggregation for graph summarization. In: SIGMOD, pp. 567–580 (2008)
Vanetik, N., Gudes, E., Shimony, S.E.: Computing frequent graph patterns from semistructured data. In: ICDM, p. 458 (2002)
Wang, C., Parthasarathy, S.: Summarizing itemset patterns using probabilistic models. In: KDD, pp. 730–735 (2006)
Yan, X., Cheng, H., Han, J., Xin, D.: Summarizing itemset patterns: a profile-based approach. In: KDD, pp. 314–323 (2005)
Yan, X., Han, J.: gspan: Graph-based substructure pattern mining. In: ICDM, p. 721 (2002)
Yan, X., Han, J.: Closegraph: mining closed frequent graph patterns. In: KDD, pp. 286–295 (2003)
Yan, X., Yu, P.S., Han, J.: Graph indexing based on discriminative frequent structure analysis. ACM Trans. Database Syst. 30(4), 960–993 (2005)
Zhang, N., Tian, Y., Patel, J.: Discovery-driven graph summarization. In: ICDE, pp. 880–891 (2010)
Zhang, S., Yang, J., Li, S.: Ring: An integrated method for frequent representative subgraph mining. In: ICDM, pp. 1082–1087 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, Z., Jin, R., Cheng, H., Yu, J.X. (2013). Frequent Subgraph Summarization with Error Control. In: Wang, J., Xiong, H., Ishikawa, Y., Xu, J., Zhou, J. (eds) Web-Age Information Management. WAIM 2013. Lecture Notes in Computer Science, vol 7923. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38562-9_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-38562-9_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38561-2
Online ISBN: 978-3-642-38562-9
eBook Packages: Computer ScienceComputer Science (R0)