Abstract
As global Internet business has been evolving, large-scale graphs are becoming popular. PageRank computation on the large-scale graphs using Hadoop with default data partitioning method suffers from poor performance because Hadoop scatters even a set of directly connected vertices to arbitrary multiple nodes. In this paper we propose a semi-clustering scheme to address this problem and improve the performance of PageRank on Hadoop. Our scheme divides a graph into a set of semi-clusters, each of which consists of connected vertices, and assigns a semi-cluster to a single data partition in order to reduce the cost of data exchange between nodes during the computation of PageRank. The semi-clusters are merged and split before the PageRank computation, in order to evenly distribute a large-scale graph into a number of data partitions. Our semi-clustering scheme drastically improves the performance: total elapsed time including the cost of the semi-clustering computation reduced by up to 36%. Furthermore, the effectiveness of our scheme increases as the size of the graph increases.
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Hong, S., Lee, J., Chang, J., Choi, D.H. (2014). A Semi-clustering Scheme for High Performance PageRank on Hadoop. In: Indulska, M., Purao, S. (eds) Advances in Conceptual Modeling. ER 2014. Lecture Notes in Computer Science, vol 8823. Springer, Cham. https://doi.org/10.1007/978-3-319-12256-4_4
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DOI: https://doi.org/10.1007/978-3-319-12256-4_4
Publisher Name: Springer, Cham
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