FAST-PPR: Scaling Personalized PageRank Estimation for Large Graphs

Abstract

We propose a new algorithm, FAST-PPR, for computing personalized PageRank: given start node <i>s</i> and target node <i>t</i> in a directed graph, and given a threshold &#948;, it computes the Personalized PageRank &#960;_s(t) from <i>s</i> to <i>t</i>, guaranteeing that the relative error is small as long &#960;<sub><i>s</i></sub>(<i>t</i>) &#62; &#948;. Existing algorithms for this problem have a running-time of &#937;(1/&#948; in comparison, FAST-PPR has a provable average running-time guarantee of <i>O</i>(&#8730;<i>d</i>/&#948;) (where <i>d</i> is the average in-degree of the graph). This is a significant improvement, since &#948; is often <i>O</i>(1/<i>n</i>) (where <i>n</i> is the number of nodes) for applications. We also complement the algorithm with an &#937;(1/&#8730;&#948;) lower bound for PageRank estimation, showing that the dependence on &#948; cannot be improved. We perform a detailed empirical study on numerous massive graphs, showing that FAST-PPR dramatically outperforms existing algorithms. For example, on the 2010 Twitter graph with 1.5 billion edges, for target nodes sampled by popularity, FAST-PPR has a 20 factor speedup over the state of the art. Furthermore, an enhanced version of FAST-PPR has a 160 factor speedup on the Twitter graph, and is at least 20 times faster on all our candidate graphs.

DOI: 10.1145/2623330.2623745

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Cite this paper

@inproceedings{Lofgren2014FASTPPRSP, title={FAST-PPR: Scaling Personalized PageRank Estimation for Large Graphs}, author={Peter Lofgren and Siddhartha Banerjee and Ashish Goel and Seshadhri Comandur}, booktitle={KDD}, year={2014} }