Abstract
A multi-graph is a set consisting of multiple graphs. Multi-graph similarity search aims to find the multi-graphs similar to the query multi-graphs from the multi-graph datasets. It plays important role in a wide range of application fields, such as finding similar drugs, searching similar molecule groups and so on. However, existing algorithms of multi-graph similarity search are memory-based algorithms, which are not suitable for the large amount of multi-graph scenarios. In this paper, we propose a parallel algorithm based on the MapReduce programming model to solve the problem of the large-scale multi-graph similarity search. Our proposed algorithm consists of two MapReduce jobs, one for indexing and the other for filtering and validation. Specially, we adapt the localization strategy to further improve the performance of our algorithm, which not only reduces the communication cost, but also mitigates the load imbalance. Extensive experimental results show that our algorithm is effective and efficient.
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Acknowledgment
The work is partially supported by the National Natural Science Foundation of China (No. 61702381, No. 61872070, No. 61772124), the Hubei Natural Science Foundation (No. 2017CFB196), Guangdong Province Key Laboratory of Popular High Performance Computers (No. 2017B030314073), Liao Ning Revitalization Talents Program (XLYC1807158), the Scientific Research Foundation of Wuhan University of Science and Technology (2017xz015), and the Fundamental Research Funds for the Central Universities (N171605001).
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Pang, J., Yu, M., Gu, Y. (2019). Efficient Large-Scale Multi-graph Similarity Search Using MapReduce. In: Ni, W., Wang, X., Song, W., Li, Y. (eds) Web Information Systems and Applications. WISA 2019. Lecture Notes in Computer Science(), vol 11817. Springer, Cham. https://doi.org/10.1007/978-3-030-30952-7_23
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DOI: https://doi.org/10.1007/978-3-030-30952-7_23
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