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Meta Paths and Meta Structures: Analysing Large Heterogeneous Information Networks Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Time-Constrained Graph Pattern Matching in a Large Temporal Graph

Authors : Yanxia Xu, Jinjing Huang, An Liu, Zhixu Li, Hongzhi Yin, Lei Zhao

Published in: Web and Big Data

Publisher: Springer International Publishing

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Abstract

Graph pattern matching (GPM) is an important operation on graph computation. Most existing work assumes that query graph or data graph is static, which is contrary to the fact that graphs in real life are intrinsically dynamic. Therefore, in this paper, we propose a new problem of Time-Constrained Graph Pattern Matching (TCGPM) in a large temporal graph. Different from traditional work, our work deals with temporal graphs rather than a series of snapshots. Besides, the query graph in TCGPM contains two types of time constraints which are helpful for finding more useful subgraphs. To address the problem of TCGPM, a baseline method and an improved method are proposed. Besides, to further improve the efficiency, two pruning rules are proposed. The improved method runs several orders of magnitude faster than the baseline method. The effectiveness of TCGPM is several orders of magnitude better than that of GPM. Extensive experiments on three real and semi-real datasets demonstrate high performance of our proposed methods.

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previous chapter A Community-Aware Approach to Minimizing Dissemination in Graphs
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Footnotes
Literature
1.
Bruno, N., Koudas, N., Srivastava, D.: Holistic twig joins: optimal XML pattern matching. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, Wisconsin, pp. 310–321. ACM (2002)
2.
Fan, W., Li, J., Ma, S., Tang, N., Wu, Y., Wu, Y.: Graph pattern matching: from intractable to polynomial time. Proc. VLDB Endow. 3(1), 264–275 (2010)CrossRef
3.
Tian, Y., McEachin, R.C., Santos, C., States, D.J., Patel, M.: SAGA: a subgraph matching tool for biological graphs. Bioinformatics 23(2), 232–239 (2007)CrossRef
4.
Cordella, L.P., Foggia, P., Sansone, C., Vento, M.: A (sub)graph isomorphism algorithm for matching large graphs. IEEE Trans. Pattern Anal. Mach. Intell. 26(10), 1367–1372 (2004)CrossRef
5.
He, H., Singh, A.K.: Graphs-at-a-time: query language and access methods for graph databases. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, Canada, pp. 405–418. ACM (2008)
6.
Shang, H., Zhang, Y., Lin, X., Yu, J.X.: Taming verification hardness: an efficient algorithm for testing subgraph isomorphism. Proc. VLDB Endow. 1(1), 364–375 (2008)CrossRef
7.
Zhao, P., Han, J.: On graph query optimization in large networks. Proc. VLDB Endow. 3(3), 340–351 (2010)CrossRef
8.
Han, W., Lee, J., Lee, J.H.: Turbo\(_{\text{iso}}\): towards ultrafast and robust subgraph isomorphism search in large graph databases. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 337–348. ACM, New York (2013)
9.
Fan, W., Wang, X., Wu, Y., Deng, D.: Distributed graph simulation: impossibility and possibility. Proc. VLDB Endow. 7(12), 1083–1094 (2014)CrossRef
10.
Chen, L., Wang, C.: Continuous subgraph pattern search over certain and uncertain graph streams. IEEE Trans. Knowl. Data Eng. 22(8), 1093–1109 (2010)CrossRef
11.
12.
Song, C., Ge, T., Chen, C.X., Wang, J.: Event pattern matching over graph streams. Proc. VLDB Endow. 8(4), 413–424 (2014)CrossRef
13.
Khurana, U., Deshpande, A.: Storing and analyzing historical graph data at scale. In: Proceedings of the 19th International Conference on Extending Database Technology, France, pp. 65–76 (2016)
14.
Fan, W., Wang, X., Wu, Y.: Diversified top-k graph pattern matching. Proc. VLDB Endow. 6(13), 1510–1521 (2013)CrossRef
15.
Allen, J., Allen, J.: Maintaining knowledge about temporal intervals. Commun. ACM 26(11), 832–843 (1983)CrossRefMATH
16.
Jin, R., McCallen, S., Liu, C., Xiang, Y., Almaas, E., Zhou, X.J.: Identifying dynamic network modules with temporal and spatial constraints. In: Proceedings of the Pacific Symposium, USA, pp. 203–214 (2009)
17.
18.
Huang, S., Fu, A.W., Liu, R.: Minimum spanning trees in temporal graphs. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, Melbourne, pp. 419–430 (2015)
19.
Yang, Y., Yan, D., Wu, H., Cheng, J., Zhou, S., Lui, J.C.S.: Diversified temporal subgraph pattern mining. In: Proceedings of the 22nd International Conference on Knowledge Discovery and Data Mining, San Francisco, pp. 1965–1974 (2016)
20.
Wu, H., Huang, Y., Cheng, J., Li, J., Ke, Y.: Reachability and time-based path queries in temporal graphs. In: 32nd IEEE International Conference on Data Engineering, Helsinki, pp. 145–156 (2016)
21.
Wu, H., Cheng, J., Huang, S., Ke, Y., Lu, Y., Xu, Y.: Path problems in temporal graphs. Proc. VLDB Endow. 7(9), 721–732 (2014)CrossRef
22.
Kossinets, G., Kleinberg, J.M., Watts, D.J.: The structure of information pathways in a social communication network. In: Proceedings of the 14th International Conference on Knowledge Discovery and Data Mining, Las Vegas, pp. 435–443 (2008)
23.
Lee, J., Han, W., Kasperovics, R., Lee, J.: An in-depth comparison of subgraph isomorphism algorithms in graph databases. Proc. VLDB Endow. 6(2), 133–144 (2012)CrossRef
24.
Henzinger, M.R., Henzinger, T.A., Kopke, P.W.: Computing simulations on finite and infinite graphs. In: 36th Annual Symposium on Foundations of Computer Science, Milwaukee, pp. 453–462 (1995)
25.
Ma, S., Cao, Y., Fan, W., Huai, J., Wo, T.: Strong simulation: capturing topology in graph pattern matching. Trans. Database Syst. 39(1), 4 (2014)MathSciNetCrossRefMATH
26.
Yuan, D., Mitra, P., Yu, H., Giles, C.L.: Iterative graph feature mining for graph indexing. In: 28th International Conference on Data Engineering, USA, pp. 198–209 (2012)
27.
Chen, C., Yan, X., Yu, P.S., Han, J., Zhang, D., Gu, X.: Towards graph containment search and indexing. In: Proceedings of the 33rd International Conference on Very Large Data Bases, Austria, pp. 926–937 (2007)
28.
Yuan, D., Mitra, P., Yu, H., Giles, C.L.: Updating graph indices with a one-pass algorithm. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, Melbourne, pp. 1903–1916 (2015)
29.
Chen, Y., Peng, W., Lee, S.: Mining temporal patterns in time interval-based data. IEEE Trans. Knowl. Data Eng. 27(12), 3318–3331 (2015)CrossRef
30.
Metadata
Title
Time-Constrained Graph Pattern Matching in a Large Temporal Graph
Authors
Yanxia Xu
Jinjing Huang
An Liu
Zhixu Li
Hongzhi Yin
Lei Zhao
Copyright Year
2017
Book
Web and Big Data

Print ISBN: 978-3-319-63578-1

Electronic ISBN: 978-3-319-63579-8

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-63579-8_9

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