Abstract
PageRank is a link analysis method to estimate the importance of nodes in a graph, and has been successfully applied in wide range of applications. However, its computational complexity is known to be high. Besides, in many applications, only a small number of nodes are of interest. To address this problem, several methods for estimating PageRank score of a target node without accessing whole graph have been proposed. In particular, Chen et al. proposed an approach where, given a target node, subgraph containing the target is induced to locally compute PageRank score. Nevertheless, its computation is still time consuming due to the fact that a number of iterative processes are required when constructing a subgraph for subsequent PageRank estimation. To make it more efficient, we propose an improved approach in which a subgraph is recursively expanded by solving a linear system without any iterative computation. To assess the efficiency of the proposed scheme, we conduct a set of experimental evaluations. The results reveal that our proposed scheme can estimate PageRank score more efficiently than the existing approach while maintaining the estimation accuracy.
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Sakakura, Y., Yamaguchi, Y., Amagasa, T., Kitagawa, H. (2014). An Improved Method for Efficient PageRank Estimation. In: Decker, H., Lhotská, L., Link, S., Spies, M., Wagner, R.R. (eds) Database and Expert Systems Applications. DEXA 2014. Lecture Notes in Computer Science, vol 8645. Springer, Cham. https://doi.org/10.1007/978-3-319-10085-2_19
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DOI: https://doi.org/10.1007/978-3-319-10085-2_19
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