Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

8-2013

Abstract

As Personalized PageRank has been widely leveraged for ranking on a graph, the efficient computation of Personalized PageRank Vector (PPV) becomes a prominent issue. In this paper, we propose FastPPV, an approximate PPV computation algorithm that is incremental and accuracy-aware. Our approach hinges on a novel paradigm of scheduled approximation: the computation is partitioned and scheduled for processing in an "organized" way, such that we can gradually improve our PPV estimation in an incremental manner, and quantify the accuracy of our approximation at query time. Guided by this principle, we develop an efficient hub based realization, where we adopt the metric of hub-length to partition and schedule random walk tours so that the approximation error reduces exponentially over iterations. Furthermore, as tours are segmented by hubs, the shared substructures between different tours (around the same hub) can be reused to speed up query processing both within and across iterations. Finally, we evaluate FastPPV over two real-world graphs, and show that it not only significantly outperforms two state-of-the-art baselines in both online and offline phrases, but also scale well on larger graphs. In particular, we are able to achieve near-constant time online query processing irrespective of graph size.

Keywords

Approximation errors, Computation algorithm, Constant time, Efficient computation, Graph sizes, Personalized PageRank, Random Walk, Real-world graphs

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

Proceedings of the VLDB Endowment: 39th International Conference on Very Large Data Bases 2013, Trento, Italy, August 26-30

Volume

6

First Page

481

Last Page

492

Identifier

10.14778/2536336.2536348

Publisher

VLDB Endowment

City or Country

Saratoga, CA

Additional URL

https://doi.org/10.14778/2536336.2536348

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