Efficient personalized pagerank with accuracy assurance

@inproceedings{Fujiwara2012EfficientPP,
  title={Efficient personalized pagerank with accuracy assurance},
  author={Yasuhiro Fujiwara and Makoto Nakatsuji and Takeshi Yamamuro and Hiroaki Shiokawa and Makoto Onizuka},
  booktitle={KDD},
  year={2012}
}
Personalize PageRank (PPR) is an effective relevance (proximity) measure in graph mining. The goal of this paper is to efficiently compute single node relevance and top-k/highly relevant nodes without iteratively computing the relevances of all nodes. Based on a "random surfer model", PPR iteratively computes the relevances of all nodes in a graph until convergence for a given user preference distribution. The problem with this iterative approach is that it cannot compute the relevance of just… CONTINUE READING
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