Strong Localization in Personalized PageRank Vectors

被引：12

作者：

Nassar, Huda ^{[1
]}

Kloster, Kyle ^{[2
]}

Gleich, David F. ^{[1
]}

机构：

[1] Purdue Univ, Dept Comp Sci, W Lafayette, IN 47907 USA

[2] Purdue Univ, Dept Math, W Lafayette, IN 47907 USA

来源：

ALGORITHMS AND MODELS FOR THE WEB GRAPH, (WAW 2015) | 2015年 / 9479卷

基金：

美国国家科学基金会;

关键词：

PageRank; Diffusion; Local algorithms;

D O I：

10.1007/978-3-319-26784-5_15

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The personalized PageRank diffusion is a fundamental tool in network analysis tasks like community detection and link prediction. It models the spread of a quantity from a set of seed nodes, and it has been observed to stay localized near this seed set. We derive an upper-bound on the number of entries necessary to approximate a personalized PageRank vector in graphs with skewed degree sequences. This bound shows localization under mild assumptions on the maximum and minimum degrees. Experimental results on random graphs with these degree sequences show the bound is loose and support a conjectured bound.

引用

页码：190 / 202

页数：13

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