Locality Sensitive Hashing for Optimizing Subgraph Query Processing in Parallel Computing Systems
Authors: 
Peng Peng, Shengyi Ji, Zhen Tian, Hongbo Jiang, Weiguo Zheng, Xuecang ZhangAuthors Info & Claims
Peng Peng, Shengyi Ji, Zhen Tian, Hongbo Jiang, Weiguo Zheng, Xuecang ZhangAuthors Info & ClaimsPages 1885 - 1896
Abstract
This paper explores parallel computing systems for efficient subgraph query processing in large graphs. We investigate how to take advantage of the inherent parallelism of parallel computing systems for both intraquery and interquery optimization during subgraph query processing. Rather than relying on widely-used hash-based methods, we utilize and extend locality sensitive hashing methods. For intraquery optimization, we use the structures of both the data graph and subgraph query to design a query-constraint locality sensitive hashing method named QCMH, which can be used to merge multiple tasks during a single subgraph query processing. For interquery optimization, we propose a query locality sensitive hashing method named QMH, which can be used to detect common subgraphs among different subgraph queries, thereby merging multiple subgraph queries. Our proposed methods can reduce the redundant computation among multiple tasks duringa single subgraph query processing or multiple queries. Extensive experimental studies on large real and synthetic graphs show that our proposed methods can improve query performance compared to state-of-the-art methods by 10% to 50%.
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Index Terms
- Locality Sensitive Hashing for Optimizing Subgraph Query Processing in Parallel Computing Systems
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Published In
August 2023
5996 pages
ISBN:9798400701030
DOI:10.1145/3580305
- General Chairs:
- Ambuj Singh,
- Yizhou Sun,
- Program Chairs:
- Leman Akoglu,
- Dimitrios Gunopulos,
- Xifeng Yan,
- Ravi Kumar,
- Fatma Ozcan,
- Jieping Ye
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Published: 04 August 2023
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- NSFC
- Science and Technology Major Projects of Changsha City
- National Key R\&D Projects
- Hunan Provincial Natural Science Foundation of China
- Technology Projects of Hunan Province
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KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
August 6 - 10, 2023
CA, Long Beach, USA
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