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人工知能学会論文誌
Vol. 30 (2015) No. 2 一般論文/2014年度大会速報論文特集 p. 473-478

記事言語:

http://doi.org/10.1527/tjsai.30.473

速報論文

In AI communities, many applications utilize PageRank. To obtain high PageRank score nodes, the original approach iteratively computes the PageRank score of each node until convergence from the whole graph. If the graph is large, this approach is infeasible due to its high computational cost. The goal of this study is to find top-k PageRank score nodes efficiently for a given graph without sacrificing accuracy. Our solution, F-Rank, is based on two ideas: (1) It iteratively estimates lower/upper bounds of PageRank scores, and (2) It constructs subgraphs in each iteration by pruning unnecessary nodes and edges to identify top-k nodes. Experiments show that F-Rank finds top-k nodes much faster than the original approach.

Copyright © 人工知能学会 2015

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