Abstract
The graph similarity join retrieves all pairs of similar graphs on graph datasets. In this paper, we propose an efficient MapReduce-friendly algorithm tackling with the graph similarity join problem on large-scale graph datasets. In particular, the efficiency of our algorithm is guaranteed by: 1) scalable prefix-filtering suitable for q-gram alphabet that is beyond the memory; 2) an effective candidate reduction strategy that greatly cuts down the data communication cost; 3) a two-round data access proposal that reduces the data access overhead. Extensive experiments on large-scale real and synthetic datasets demonstrate that our proposal outperforms the state-of-the-art method with higher system scalability and faster speed.
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© 2014 Springer International Publishing Switzerland
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Pang, J., Gu, Y., Xu, J., Bao, Y., Yu, G. (2014). Efficient Graph Similarity Join with Scalable Prefix-Filtering Using MapReduce. In: Li, F., Li, G., Hwang, Sw., Yao, B., Zhang, Z. (eds) Web-Age Information Management. WAIM 2014. Lecture Notes in Computer Science, vol 8485. Springer, Cham. https://doi.org/10.1007/978-3-319-08010-9_43
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DOI: https://doi.org/10.1007/978-3-319-08010-9_43
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08009-3
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