FAST-PPR: scaling personalized pagerank estimation for large graphs

@article{Lofgren2014FASTPPRSP,
  title={FAST-PPR: scaling personalized pagerank estimation for large graphs},
  author={P. Lofgren and Siddhartha Banerjee and Ashish Goel and Seshadhri Comandur},
  journal={Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining},
  year={2014}
}
We propose a new algorithm, FAST-PPR, for computing personalized PageRank: given start node s and target node t in a directed graph, and given a threshold δ, it computes the Personalized PageRank π_s(t) from s to t, guaranteeing that the relative error is small as long πs(t) > δ. Existing algorithms for this problem have a running-time of Ω(1/δ in comparison, FAST-PPR has a provable average running-time guarantee of O(√d/δ) (where d is the average in-degree of the graph). This is a significant… Expand
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Fast-ppr: Scaling personalized pagerank estimation for large graphs
  • tech. rep., Stanford University, 2014. arXiv preprint arXiv:1404.3181.
  • 2014