Node-Weighted Personalized PageRank Algorithm

A Node-Weighted Personalized PageRank Algorithm is a Personalized PageRank Algorithm in which the node weights are personalized.

• Example(s):
  • …
• Counter-Example(s):
  • PPRGo Algorithm,
  • Edge-Weighted Personalized PageRank Algorithm,
  • Personalized Pagerank with Accuracy Assurance Algorithm.
• See: Random Walk Process, Random Graph Walk, PageRank Algorithm, PageRank Value, Random Walk NLP Algorithm, Random Walk Image Processing Algorithm.

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References

2015

• (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.
  • QUOTE: Through these edge and node weights, PageRank can be personalized to particular users or queries. Concretely, in node-weighted personalized PageRank, we solve
    $Mx(w)=(1-\alpha)v(w),\quad w\in \R^d$

    where $w$ is a vector of personalization parameters. In edge-weighted personalized PageRank, we solve

    $M(w)x(w)=(1-\alpha)v,\quad w\in \R^d$

    For example, the personalization vector can specify the topic preference for the query, so the random walker will teleport to nodes associated with preferred topics (node-weighted personalized PageRank) or move through edges associated with preferred topics more frequently (edge-weighted personalized PageRank).

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