Speeding Up Subgraph Matching Queries with Schema Guided Index
Authors: 
Yating Zhao, Wenhan Wang, Shuxin Chen, Bocheng Zhang, Jiajun Wang, Zhaokang WangAuthors Info & Claims
Yating Zhao, Wenhan Wang, Shuxin Chen, Bocheng Zhang, Jiajun Wang, Zhaokang WangAuthors Info & ClaimsPages 34 - 38
Abstract
The performance optimization of existing subgraph matching methods mainly focuses on the topological structure of graphs, with little consideration of the rich schema information provided by complex graphs. This work proposes a Schema Guided Index (SGI) to speed up subgraph matching by utilizing the multiplicity of edge labels provided by the graph schema. SGI presents a DFS-based algorithm to extract many-to-one index paths in the schema and adds extra index edges to graph datasets according to index paths. SGI simplifies query graphs with index edges to reduce computation costs during matching. Experimental results on the LDBC SNB datasets demonstrate that the construction costs of SGI increase near-linearly with the number of edges in the datasets. SGI can reduce the average query response time by up to 36.0%.
References
[1]
Bibek Bhattarai, Hang Liu, and H. Howie Huang. 2019. CECI: Compact Embedding Cluster Index for Scalable Subgraph Matching. In Proceedings of the 2019 International Conference on Management of Data. ACM, 1447–1462. https://doi.org/10.1145/3299869.3300086
[2]
Fei Bi, Lijun Chang, Xuemin Lin, Lu Qin, and Wenjie Zhang. 2016. Efficient Subgraph Matching by Postponing Cartesian Products. In Proceedings of the 2016 International Conference on Management of Data. ACM, 1199–1214. https://doi.org/10.1145/2882903.2915236
[3]
Vincenzo Bonnici, Rosalba Giugno, Alfredo Pulvirenti, Dennis E. Shasha, and Alfredo Ferro. 2013. A subgraph isomorphism algorithm and its application to biochemical data. BMC Bioinform. 14, S-7 (2013), S13. https://doi.org/10.1186/1471-2105-14-S7-S13
[4]
Myoungji Han, Hyunjoon Kim, Geonmo Gu, Kunsoo Park, and Wook-Shin Han. 2019. Efficient Subgraph Matching: Harmonizing Dynamic Programming, Adaptive Matching Order, and Failing Set Together. In Proceedings of the 2019 International Conference on Management of Data. ACM, 1429–1446. https://doi.org/10.1145/3299869.3319880
[5]
Alpár Jüttner and Péter Madarasi. 2018. VF2++ - An improved subgraph isomorphism algorithm. Discret. Appl. Math. 242 (2018), 69–81. https://doi.org/10.1016/J.DAM.2018.02.018
[6]
Chathura Kankanamge, Siddhartha Sahu, Amine Mhedhbi, Jeremy Chen, and Semih Salihoglu. 2017. Graphflow: An Active Graph Database. In Proceedings of the 2017 ACM International Conference on Management of Data. ACM, 1695–1698. https://doi.org/10.1145/3035918.3056445
[7]
Hyunjoon Kim, Yunyoung Choi, Kunsoo Park, Xuemin Lin, Seok-Hee Hong, and Wook-Shin Han. 2023. Fast subgraph query processing and subgraph matching via static and dynamic equivalences. VLDB J. 32, 2 (2023), 343–368. https://doi.org/10.1007/S00778-022-00749-X
[8]
Longbin Lai, Lu Qin, Xuemin Lin, and Lijun Chang. 2015. Scalable Subgraph Enumeration in MapReduce. Proc. VLDB Endow. 8, 10 (2015), 974–985. https://doi.org/10.14778/2794367.2794368
[9]
Longbin Lai, Lu Qin, Xuemin Lin, Ying Zhang, and Lijun Chang. 2016. Scalable Distributed Subgraph Enumeration. Proc. VLDB Endow. 10, 3 (2016), 217–228. https://doi.org/10.14778/3021924.3021937
[10]
Longbin Lai, Zhu Qing, Zhengyi Yang, Xin Jin, Zhengmin Lai, Ran Wang, Kongzhang Hao, Xuemin Lin, Lu Qin, Wenjie Zhang, Ying Zhang, Zhengping Qian, and Jingren Zhou. 2019. Distributed Subgraph Matching on Timely Dataflow. Proc. VLDB Endow. 12, 10 (2019), 1099–1112. https://doi.org/10.14778/3339490.3339494
[11]
Longbin Lai, Yufan Yang, Zhibin Wang, Yuxuan Liu, Haotian Ma, Sijie Shen, Bingqing Lyu, Xiaoli Zhou, Wenyuan Yu, Zhengping Qian, Chen Tian, Sheng Zhong, Yeh-Ching Chung, and Jingren Zhou. 2023. GLogS: Interactive Graph Pattern Matching Query At Large Scale. In 2023 USENIX Annual Technical Conference. USENIX Association, 53–69. https://www.usenix.org/conference/atc23/presentation/lai
[12]
Daniel Mawhirter and Bo Wu. 2019. AutoMine: harmonizing high-level abstraction and high performance for graph mining. In Proceedings of the 27th ACM Symposium on Operating Systems Principles. ACM, 509–523. https://doi.org/10.1145/3341301.3359633
[13]
Jaroslav Pokorný. 2016. Conceptual and Database Modelling of Graph Databases. In Proceedings of the 20th International Database Engineering & Applications Symposium. ACM, 370–377. https://doi.org/10.1145/2938503.2938547
[14]
Haichuan Shang, Ying Zhang, Xuemin Lin, and Jeffrey Xu Yu. 2008. Taming verification hardness: an efficient algorithm for testing subgraph isomorphism. Proc. VLDB Endow. 1, 1 (2008), 364–375. https://doi.org/10.14778/1453856.1453899
[15]
Shixuan Sun and Qiong Luo. 2020. In-Memory Subgraph Matching: An In-depth Study. In Proceedings of the 2020 International Conference on Management of Data. ACM, 1083–1098. https://doi.org/10.1145/3318464.3380581
[16]
Gábor Szárnyas, Jack Waudby, Benjamin A. Steer, Dávid Szakállas, Altan Birler, Mingxi Wu, Yuchen Zhang, and Peter A. Boncz. 2022. The LDBC Social Network Benchmark: Business Intelligence Workload. Proc. VLDB Endow. 16, 4 (2022), 877–890. https://doi.org/10.14778/3574245.3574270
[17]
Zhaokang Wang, Rong Gu, Weiwei Hu, Chunfeng Yuan, and Yihua Huang. 2019. BENU: Distributed Subgraph Enumeration with Backtracking-Based Framework. In Proceedings of the 35th IEEE International Conference on Data Engineering (ICDE 2019). IEEE, 136–147. https://doi.org/10.1109/ICDE.2019.00021
[18]
Zhaokang Wang, Weiwei Hu, Guowang Chen, Chunfeng Yuan, Rong Gu, and Yihua Huang. 2021. Towards Efficient Distributed Subgraph Enumeration Via Backtracking-Based Framework. IEEE Trans. Parallel Distributed Syst. 32, 12 (2021), 2953–2969. https://doi.org/10.1109/TPDS.2021.3076246
[19]
Zhengyi Yang, Longbin Lai, Xuemin Lin, Kongzhang Hao, and Wenjie Zhang. 2021. HUGE: An Efficient and Scalable Subgraph Enumeration System. In Proceedings of the 2021 International Conference on Management of Data. ACM, 2049–2062. https://doi.org/10.1145/3448016.3457237
Index Terms
- Speeding Up Subgraph Matching Queries with Schema Guided Index
Recommendations
A subgraph matching algorithm based on subgraph index for knowledge graph
AbstractThe problem of subgraph matching is one fundamental issue in graph search, which is NP-Complete problem. Recently, subgraph matching has become a popular research topic in the field of knowledge graph analysis, which has a wide range of ...
Versatile Equivalences: Speeding up Subgraph Query Processing and Subgraph Matching
SIGMOD '21: Proceedings of the 2021 International Conference on Management of DataSubgraph query processing (also known as subgraph search) and subgraph matching are fundamental graph problems in many application domains. A lot of efforts have been made to develop practical solutions for these problems. Despite the efforts, existing ...
Subgraph Matching with Set Similarity in a Large Graph Database
In real-world graphs such as social networks, Semantic Web and biological networks, each vertex usually contains rich information, which can be modeled by a set of tokens or elements. In this paper, we study a subgraph matching with set similarity (SMS<...
Comments
Information & Contributors
Information
Published In
June 2024
217 pages
ISBN:9798400717048
DOI:10.1145/3672121
Copyright © 2024 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 01 August 2024
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
CNCIT 2024
CNCIT 2024: 2024 3rd International Conference on Networks, Communications and Information Technology
June 7 - 9, 2024
Xi'an, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 19Total Downloads
- Downloads (Last 12 months)19
- Downloads (Last 6 weeks)3
Reflects downloads up to 08 Feb 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format