Abstract
Graph similarity search is a common operation of graph database, and graph editing distance constraint is the most common similarity measure to solve graph similarity search problem. However, accurate calculation of graph editing distance is proved to be NP hard, and the filter and verification framework are adopted in current method. In this paper, a dictionary tree based clustering index structure is proposed to reduce the cost of candidate graph, and is verified in the filtering stage. An efficient incremental partition algorithm was designed. By calculating the distance between query graph and candidate graph partition, the filtering effect was further enhanced. Experiments on real large graph datasets show that the performance of this algorithm is significantly better than that of the existing algorithms.
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Shang, H., Lin, X., et al.: Connected substructure similarity search. In: SIGMOD 2010, pp. 903–914 (2010)
Gouda, K., Hassaan, M.: CSI_GED: an efficient approach for graph edit similarity computation. In: ICDE 2016, pp. 265–276 (2016)
Zhu, G., Lin, X., et al.: TreeSpan: efficiently computing similarity all-matching. In: SIGMOD 2012, pp. 529–540 (2012)
Maergner, P., Riesen, K., et al.: A structural approach to offline signature verification using graph edit distance. In: ICDAR 2017, pp. 1216–1222 (2017)
Geng, C., Jung, Y., et al.: iScore: a novel graph kernel-based function for scoring protein-protein docking models. Bioinformatics 36(1), 112–121 (2020)
Zeng, Z., Tung, A.K.H., et al.: Comparing stars: on approximating graph edit distance. PVLDB 2(1), 25–36 (2009)
Riesen, K., Emmenegger, S., Bunke, H.: A novel software toolkit for graph edit distance computation. In: Kropatsch, W.G., Artner, N.M., Haxhimusa, Y., Jiang, X. (eds.) GbRPR 2013. LNCS, vol. 7877, pp. 142–151. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38221-5_15
Wang, G., Wang, B., et al.: Efficiently indexing large sparse graphs for similarity search. IEEE Trans. Knowl. Data Eng. 24(3), 440–451 (2012)
Zhao, X., Xiao, C., Lin, X., Wang, W., Ishikawa, Y.: Efficient processing of graph similarity queries with edit distance constraints. VLDB J. 22(6), 727–752 (2013). https://doi.org/10.1007/s00778-013-0306-1
Zheng, W., Zou, L., et al.: Efficient graph similarity search over large graph databases. IEEE Trans. Knowl. Data Eng. 27(4), 964–978 (2015)
Ullmann, J.R.: Degree reduction in labeled graph retrieval. ACM J. Exp. Algorithmics 20, 1.3:1.1–1.3:1.54 (2015)
Li, Z., Jian, X., et al.: An efficient probabilistic approach for graph similarity search. In: ICDE 2018, pp. 533–544 (2018)
Zhao, X., Xiao, C., et al.: A partition-based approach to structure similarity search. PVLDB 7(3), 169–180 (2013)
Liang, Y., Zhao, P.: Similarity search in graph databases: a multi-layered indexing approach. In: ICDE 2017, pp. 783–794 (2017)
Kim, J., Choi, D.-H., Li, C.: Inves: incremental partitioning-based verification for graph similarity search. In: EDBT 2019, pp. 229–240 (2019)
Acknowledgment
The Natural Science Foundation of Heilongjiang Province under Grant Nos. F2018028. Received 2000-00-00, Accepted 2000-00-00.
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Li, Y., Yang, Y., Zhong, Y. (2020). An Incremental Partitioning Graph Similarity Search Based on Tree Structure Index. In: Zeng, J., Jing, W., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2020. Communications in Computer and Information Science, vol 1257. Springer, Singapore. https://doi.org/10.1007/978-981-15-7981-3_2
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DOI: https://doi.org/10.1007/978-981-15-7981-3_2
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