2015 EdgeWeightedPersonalizedPageRan

• (Xie et al., 2015) ⇒ Wenlei Xie, David Bindel, Alan Demers, and Johannes Gehrke. (2015). "Edge-Weighted Personalized PageRank: Breaking A Decade-Old Performance Barrier". In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2015). ISBN:978-1-4503-3664-2 doi:10.1145/2783258.2783278

Subject Headings: Personalized PageRank, Edge-Weighted Personalized PageRank Algorithm.

Cited By

• Google Scholar: ~ 32 Citations.
• ACM DL: ~ 16 Citations.

Quotes

Author Keywords

• Data mining; Personalized PageRank.

Abstract

Personalized PageRank is a standard tool for finding vertices in a graph that are most relevant to a query or user. To personalize PageRank, one adjusts node weights or edge weights that determine teleport probabilities and transition probabilities in a random surfer model. There are many fast methods to approximate PageRank when the node weights are personalized; however, personalization based on edge weights has been an open problem since the dawn of personalized PageRank over a decade ago. In this paper, we describe the first fast algorithm for computing PageRank on general graphs when the edge weights are personalized. Our method, which is based on model reduction, outperforms existing methods by nearly five orders of magnitude. This huge performance gain over previous work allows us --- for the very first time --- to solve learning-to-rank problems for edge weight personalization at interactive speeds, a goal that had not previously been achievable for this class of problems.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2015 EdgeWeightedPersonalizedPageRan	Johannes Gehrke Wenlei Xie David Bindel Alan Demers			Edge-Weighted Personalized PageRank: Breaking A Decade-Old Performance Barrier				10.1145/2783258.2783278		2015