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A matrix sampling approach for efficient SimRank computation
Lu,Juan1; Gong,Zhiguo2; Yang,Yiyang3
2021-05-01
Source PublicationInformation Sciences
ISSN0020-0255
Volume556Pages:1-26
AbstractEvaluating similarities between node pairs in a graph is an important task for data analytics and mining. Among various similarity measures proposed in recent years, SimRank is regarded as one of the most influential measures. However, the computation of SimRank is very expensive especially for large graphs. Although pruning technique and random walk based methods were proposed to accelerate the computation, the accuracy of SimRank score is still very low. In this paper, we propose a novel matrix random sampling approach to accelerate computation speed and reduce memory cost. The matrix random sampling technique not only guarantees the sparsity of the involved matrices, but also enhances the precision of estimated SimRank scores. Moreover, we design a fast sparse matrix–matrix multiplication technique which makes the time complexity of single-source query free of the graph size. We further exploit the Steepest Decent technique to accelerate the speed of convergence. The experimental results show our proposed algorithms outperform the state-of-the-art SimRank algorithms.
KeywordMatrix sampling SimRank Steepest descent
DOI10.1016/j.ins.2020.12.046
URLView the original
Language英语
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Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorLu,Juan
Affiliation1.Beijing Institute of Petrochemical Technology,Beijing,China
2.University of Macau,Macau,China
3.GuangDong University of Technology,Guangzhou,China
Recommended Citation
GB/T 7714
Lu,Juan,Gong,Zhiguo,Yang,Yiyang. A matrix sampling approach for efficient SimRank computation[J]. Information Sciences,2021,556:1-26.
APA Lu,Juan,Gong,Zhiguo,&Yang,Yiyang.(2021).A matrix sampling approach for efficient SimRank computation.Information Sciences,556,1-26.
MLA Lu,Juan,et al."A matrix sampling approach for efficient SimRank computation".Information Sciences 556(2021):1-26.
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