Fast, memory-efficient low-rank approximation of SimRank

被引：3

作者：

Oseledets I.V. ^{[1
,2
]}

Ovchinnikov G.V. ^{[1
,3
,4
]}

Katrutsa A.M. ^{[1
,5
]}

机构：

[1] Skolkovo Institute of Science and Technology, Novaya St., 100, Skolkovo

[2] Institute of Numerical Mathematics, Russian Academy of Sciences, Gubkina Street, 8, Moscow

[3] Institute for Design Problems in Microelectronics, Russian Academy of Sciences, Prosp. 60-letiya Oktyabrya, 9, Moscow

[4] Innopolis University, 40-42 Profsoyuznaya str, Kazan

[5] Moscow Institute of Physics and Technology, Institutskiy Lane 9, Dolgoprudny, Moscow

来源：

Journal of Complex Networks | 2017年 / 5卷 / 01期

基金：

俄罗斯基础研究基金会; 俄罗斯科学基金会;

关键词：

Link Analysis; Lyapunov equation; Probabilistic svd; Similarity Search; SimRank; Svd;

D O I：

10.1093/comnet/cnw008

中图分类号：

学科分类号：

摘要：

SimRank is a well-known similarity measure between graph vertices. This measure relies on a graph topology and is built on the intuition that 'two objects are similar if they are related to similar objects'. Formalization of this intuition leads to a system of linear equations, so large that even the solution vector will not fit in the RAM for many real-world graphs. To solve this issue we propose a new method based on a low-rank approximation of the linear system solution. This approach requires less memory and provides a significant calculation speed-up. The experiments show that the proposed method provides an accurate SimRank approximation. © The authors 2016. Published by Oxford University Press. All rights reserved.

引用

页码：111 / 126

页数：15

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