PPRGo Algorithm

A PPRGo Algorithm is a Personalized PageRank (PPR) Algorithm based on GNNs.

• Context:
  • It was first introduced by Bojchevski et al. (2020).
• Example(s):
  • that described in Bojchevski et al. (2020),
  • …
• Counter-Example(s):
  • 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

2020

• (Bojchevski et al., 2020) ⇒ Aleksandar Bojchevski, Johannes Klicpera, Bryan Perozzi, Amol Kapoor, Martin Blais, Benedek Rózemberczki, Michal Lukasik, and Stephan Günnemann. (2020). “Scaling Graph Neural Networks with Approximate PageRank.” In: arXiv preprint arXiv:2007.01570.
  • QUOTE: We present the PPRGo model which utilizes an efficient approximation of information diffusion in GNNs resulting in significant speed gains while maintaining state-of-the-art prediction performance.

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