Abstract
Graph data is ubiquitous in many domains such as social network, bioinformatics, biochemical and image analysis. Finding subgraph isomorphism is a fundamental task in most graph databases and applications. Despite its NP-completeness, many algorithms have been proposed to tackle this problem in practical scenarios. Recently proposed algorithms consistently claimed themselves faster than previous ones, while the fairness of their evaluation is questionable due to query-set selections and algorithm implementations. Although there are some existing works comparing the performance of state-of-the-art subgraph isomorphism algorithms under the same query-sets and implementation settings, we observed there are still some important issues left unclear. For example, it remains unclear how those algorithms behave when dealing with unlabelled graphs. It is debatable that the number of embeddings of a larger query is smaller than that of a smaller query, which further challenges the remark that the time cost should decrease for a good algorithm when increasing the size of the queries. In this paper, we conducted a comprehensive evaluation of three of most recent subgraph algorithms. Through the analysis of the experiment results, we clarify those issues.
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References
Carletti, V., Foggia, P., Saggese, A., Vento, M.: Introducing VF3: a new algorithm for subgraph isomorphism. In: Foggia, P., Liu, C.-L., Vento, M. (eds.) GbRPR 2017. LNCS, vol. 10310, pp. 128–139. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-58961-9_12
Cheng, J., Ke, Y., Ng, W., Lu, A.: Fg-index: towards verification-free query processing on graph databases, pp. 857–872 (2007)
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)
Giugno, R., Shasha, D.: GraphGrep: a fast and universal method for querying graphs, vol. 2, pp. 112–115 (2002)
Han, W.S., Lee, J., Lee, J.H.: TurboISO: towards ultrafast and robust subgraph isomorphism search in large graph databases. In: SIGMOD, pp. 337–348 (2013)
He, H., Singh, A.K.: Closure-tree: an index structure for graph queries, p. 38 (2006)
He, H., Singh, A.K.: Query language and access methods for graph databases. In: Aggarwal, C., Wang, H. (eds.) Managing and Mining Graph Data. ADBS, vol. 40, pp. 125–160. Springer, Boston (2010). https://doi.org/10.1007/978-1-4419-6045-0_4
Lee, J., Han, W.S., Kasperovics, R., Lee, J.H.: An in-depth comparison of subgraph isomorphism algorithms in graph databases. VLDB 6, 133–144 (2012)
Ren, X., Wang, J.: Exploiting vertex relationships in speeding up subgraph isomorphism over large graphs. Proc. VLDB Endow. 8(5), 617–628 (2015)
Shang, H., Zhang, Y., Lin, X., Yu, J.X.: Taming verification hardness: an efficient algorithm for testing subgraph isomorphism. PVLDB 1(1), 364–375 (2008)
Sun, Z., Wang, H., Wang, H., Shao, B., Li, J.: Efficient subgraph matching on billion node graphs. PVLDB 5, 788–799 (2012)
Ullmann, J.R.: An algorithm for subgraph isomorphism. JACM 23(1), 31–42 (1976)
Yan, X., Yu, P.S., Han, J.: Graph indexing: a frequent structure-based approach, pp. 335–346 (2004)
Zhao, P., Han, J.: On graph query optimization in large networks. PVLDB 3, 340–351 (2010)
Zhao, P., Yu, J.X., Yu, P.S.: Graph indexing: tree \(+\) delta \({>}{=}\) graph, pp. 938–949 (2007)
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Ren, X., Wang, J., Franciscus, N., Stantic, B. (2018). Experimental Clarification of Some Issues in Subgraph Isomorphism Algorithms. In: Nguyen, N., Hoang, D., Hong, TP., Pham, H., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2018. Lecture Notes in Computer Science(), vol 10752. Springer, Cham. https://doi.org/10.1007/978-3-319-75420-8_7
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