Abstract
Recently, parallel computing systems composed of interconnected workers through a high-speed network have become readily available, thereby presenting an opportunity for parallelizing subgraph queries in large graphs. To effectively evaluate these subgraph queries, it is crucial to place vertices among different workers. In contrast to widely used hash-based techniques, our approach leverages the utilization of locality sensitive hashing methods for data placement. This paper introduces a novel graph locality sensitive hashing method named VMH, which is specifically designed for data placement by considering the labels of vertices. By employing VMH, we can effectively place similar vertices to the same worker while considering the labels of vertices, thereby reducing redundant communication and computation across multiple workers during parallel subgraph query evaluation. Extensive experimental studies conducted on both large real and synthetic graphs demonstrate that our proposed techniques lead to significant improvements in query performance compared to existing methods.
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
Similar content being viewed by others
Notes
References
Abbas, Z., Kalavri, V., Carbone, P., Vlassov, V.: Streaming graph partitioning: an experimental study. Proc. VLDB Endow. 11(11), 1590–1603 (2018)
Aluç, G., Hartig, O., Özsu, M.T., Daudjee, K.: Diversified stress testing of RDF data management systems. In: Mika, P., et al. (eds.) ISWC 2014. LNCS, vol. 8796, pp. 197–212. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11964-9_13
Aluç, G., Özsu, M.T., Daudjee, K.: Building self-clustering RDF databases using tunable-LSH. VLDB J. 28(2), 173–195 (2019)
Aumüller, M., Ceccarello, M.: Implementing distributed similarity joins using locality sensitive hashing. In: EDBT, pp. 1:78–1:90. OpenProceedings.org (2022)
Bi, F., Chang, L., Lin, X., Qin, L., Zhang, W.: Efficient subgraph matching by postponing cartesian products. In: SIGMOD, New York, NY, USA, pp. 1199–1214. Association for Computing Machinery (2016)
Broder, A.: On the resemblance and containment of documents. In: SEQUENCES, USA, p. 21. IEEE Computer Society (1997)
Fan, W.: Graph pattern matching revised for social network analysis. In: ICDT, New York, NY, USA, pp. 8–21. Association for Computing Machinery (2012)
Fan, W., et al.: Application driven graph partitioning. In: SIGMOD, New York, NY, USA, pp. 1765–1779. Association for Computing Machinery (2020)
Fan, W., Xu, R., Yin, Q., Yu, W., Zhou, J.: Application-driven graph partitioning. VLDB J. 32(1), 149–172 (2023)
Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. W. H. Freeman & Co., USA (1979)
Han, W.-S., Lee, J., Lee, J.-H.: Turbo\({}_{\text{iso}}\): towards ultrafast and robust subgraph isomorphism search in large graph databases. In: SIGMOD, New York, NY, USA, pp. 337–348. Association for Computing Machinery (2013)
Hu, X., Yi, K., Tao, Y.: Output-optimal massively parallel algorithms for similarity joins. ACM Trans. Database Syst. 44(2), 61–636 (2019)
Huang, J., Abadi, D.J., Ren, K.: Scalable SPARQL querying of large RDF graphs. PVLDB 4(11), 1123–1134 (2011)
Indyk, P.: Nearest neighbors in high-dimensional spaces. In: Handbook of Discrete and Computational Geometry, 2nd edn., pp. 877–892. Chapman and Hall/CRC (2004)
Ji, J., Li, J., Yan, S., Zhang, B., Tian, Q.: Super-bit locality-sensitive hashing. In: NIPS, NIPS 2012, Red Hook, NY, USA, pp. 108–116. Curran Associates Inc. (2012)
Karypis, G., Kumar, V.: A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM J. Sci. Comput. 20(1), 359–392 (1998)
Kiran, P., Sivadasan, N.: Scalable graph similarity search in large graph databases. In: 2015 IEEE Recent Advances in Intelligent Computational Systems (RAICS), pp. 207–211 (2015)
Kwak, H., Lee, C., Park, H., Moon, S.: What is Twitter, a social network or a news media? In: WWW, New York, NY, USA, pp. 591–600. Association for Computing Machinery (2010)
Lai, L., Qing, Z., Yang, Z., Jin, X., Lai, Z., Wang, R., Hao, K., Lin, X., Qin, L., Zhang, W., Zhang, Y., Qian, Z., Zhou, J.: Distributed Subgraph Matching on Timely Dataflow. Proc. VLDB Endow. 12(10), 1099–1112 (2019)
Leskovec, J., Rajaraman, A., Ullman, J.D.: Mining of Massive Datasets, 2nd edn. Cambridge University Press, Cambridge (2014)
Marçais, G., DeBlasio, D.F., Pandey, P., Kingsford, C.: Locality-sensitive hashing for the edit distance. Bioinform. 35(14), i127–i135 (2019)
Özsu, M.T., Valduriez, P.: Principles of Distributed Database Systems, 4th edn. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-26253-2
Peng, P., Ozsu, M., Zou, L., Yan, C., Liu, C.: MPC: minimum property-cut RDF graph partitioning. In: ICDE, Los Alamitos, CA, USA, pp. 192–204. IEEE Computer Society (2022)
Pržulj, N., Corneil, D.G., Jurisica, I.: Efficient estimation of graphlet frequency distributions in protein-protein interaction networks. Bioinformatics 22(8), 974–980 (2006)
Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., Su, Z.: Arnetminer: extraction and mining of academic social networks. In: SIGKDD, New York, NY, USA, pp. 990–998. Association for Computing Machinery (2008)
Yan, D., Guo, G., Chowdhury, M.M.R., Özsu, M.T., Ku, W., Lui, J.C.S.: G-thinker: a distributed framework for mining subgraphs in a big graph. In: ICDE, pp. 1369–1380, Dallas, TX, USA. IEEE (2020)
Zhang, B., Liu, X., Lang, B.: Fast graph similarity search via locality sensitive hashing. In: Ho, Y.-S., Sang, J., Ro, Y.M., Kim, J., Wu, F. (eds.) PCM 2015. LNCS, vol. 9314, pp. 623–633. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24075-6_60
Acknowledgement
This work was supported by NSFC under grants (U20A20174), Science and Technology Major Projects of Changsha City (No. kh2205032), and Hunan Provincial Natural Science Foundation of China under grant 2022JJ30165.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Li, M., Zhai, B., Jiang, Y., Li, Y., Qin, Z., Peng, P. (2024). Locality Sensitive Hashing for Data Placement to Optimize Parallel Subgraph Query Evaluation. In: Song, X., Feng, R., Chen, Y., Li, J., Min, G. (eds) Web and Big Data. APWeb-WAIM 2023. Lecture Notes in Computer Science, vol 14331. Springer, Singapore. https://doi.org/10.1007/978-981-97-2303-4_3
Download citation
DOI: https://doi.org/10.1007/978-981-97-2303-4_3
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-2302-7
Online ISBN: 978-981-97-2303-4
eBook Packages: Computer ScienceComputer Science (R0)