Title | ||
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Efficient Personalized PageRank Computation: A Spanning Forests Sampling Based Approach |
Abstract | ||
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Computing the personalized PageRank vector is a fundamental problem in graph analysis. In this paper, we propose several novel algorithms to efficiently compute the personalized PageRank vector with a decay factor a based on an interesting connection between the personalized PageRank values and the weights of random spanning forests of the graph. Such a connection is derived based on a newly-developed matrix forest theorem on graphs. Based on this, we present an efficient spanning forest sampling algorithm via simulating loop-erased a-random walks to estimate the personalized PageRank vector. Compared to all existing methods, a striking feature of our approach is that its performance is insensitive w.r.t. (with respect to) the parameter alpha. As a consequence, our algorithm is often much faster than the state-of-the-art algorithms when alpha is small, which is the demanding case for many graph analysis tasks. We show that our technique can significantly improve the efficiency of the state-of-the-art algorithms for answering two well-studied personalized PageRank queries, including single source query and single target query. Extensive experiments on seven large real-world graphs demonstrate the efficiency of the proposed method. |
Year | DOI | Venue |
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2022 | 10.1145/3514221.3526140 | PROCEEDINGS OF THE 2022 INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA (SIGMOD '22) |
Keywords | DocType | ISSN |
Personalized PageRank, Spanning Forest | Conference | 0730-8078 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 |
Name | Order | Citations | PageRank |
---|---|---|---|
Meihao Liao | 1 | 0 | 0.68 |
Rong-Hua Li | 2 | 381 | 33.77 |
Qiangqiang Dai | 3 | 0 | 2.03 |
Guoren Wang | 4 | 1366 | 159.46 |