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. © 2014 Springer International Publishing Switzerland.
CITATION STYLE
Pang, J., Gu, Y., Xu, J., Bao, Y., & Yu, G. (2014). Efficient graph similarity join with scalable prefix-filtering using mapreduce. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8485 LNCS, pp. 415–418). Springer Verlag. https://doi.org/10.1007/978-3-319-08010-9_43
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