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TBSGM: A Fast Subgraph Matching Method on Large Scale Graphs

TBSGM: A Fast Subgraph Matching Method on Large Scale Graphs

Fusheng Jin, Yifeng Yang, Shuliang Wang, Ye Xue, Zhen Yan
Copyright: © 2018 |Volume: 14 |Issue: 4 |Pages: 23
ISSN: 1548-3924|EISSN: 1548-3932|EISBN13: 9781522542674|DOI: 10.4018/IJDWM.2018100104
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MLA

Jin, Fusheng, et al. "TBSGM: A Fast Subgraph Matching Method on Large Scale Graphs." IJDWM vol.14, no.4 2018: pp.67-89. http://doi.org/10.4018/IJDWM.2018100104

APA

Jin, F., Yang, Y., Wang, S., Xue, Y., & Yan, Z. (2018). TBSGM: A Fast Subgraph Matching Method on Large Scale Graphs. International Journal of Data Warehousing and Mining (IJDWM), 14(4), 67-89. http://doi.org/10.4018/IJDWM.2018100104

Chicago

Jin, Fusheng, et al. "TBSGM: A Fast Subgraph Matching Method on Large Scale Graphs," International Journal of Data Warehousing and Mining (IJDWM) 14, no.4: 67-89. http://doi.org/10.4018/IJDWM.2018100104

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Abstract

Subgraph matching, which belongs to NP-hard, faces significant challenges on a large scale graph with billions of nodes, and existing methods are usually confronted with greater challenges from both stability and efficiency. In this article, a subgraph matching method in a distributed system, tree model-based subgraph matching method (TBSGM) is proposed. The authors provide a transformed efficient query tree as a replacement for a query graph. In order to get the tree, they present a cost evaluation model which may help to generate the efficient query tree according to network communication-cost and calculation-cost evaluation. Also, a key set based indexing strategy for intermediate results is given to simplify the matching results during network communication. Extensive experiments with real-world datasets show that TBSGM significantly outperforms other methods in the aspects of scalability and efficiency.

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