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FAST-PPR: Personalized PageRank Estimation for Large Graphs

FAST-PPR: Personalized PageRank Estimation for Large Graphs. Peter Lofgren (Stanford ) Joint work with Siddhartha Banerjee (Stanford), Ashish Goel (Stanford), and C. Seshadhri (Sandia). Motivation: Personalized Search. Motivation: Personalized Search. Re-ranked by PPR.

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FAST-PPR: Personalized PageRank Estimation for Large Graphs

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  1. FAST-PPR: Personalized PageRank Estimation for Large Graphs Peter Lofgren (Stanford) Joint work with Siddhartha Banerjee (Stanford), Ashish Goel (Stanford), and C. Seshadhri (Sandia)

  2. Motivation: Personalized Search

  3. Motivation: Personalized Search Re-ranked by PPR

  4. Result Preview 1.2 hour 6 min 2 sec Local-Update Fast-PPR Monte-Carlo

  5. Personalized PageRank

  6. Goal

  7. Previous Algorithm: Monte-Carlo Previous Algorithm: Monte-Carlo [Avrachenkov, et al 2007]

  8. Previous Algorithm: Local Update [Anderson, et al 2007]

  9. Main Result

  10. Analogy: Bidirectional Search

  11. Bidirectional PageRank Algorithm u Reverse Work (Frontier Discovery) Forward Work (Random Walks)

  12. Main Idea

  13. Experimental Setup

  14. Empirical Running Time Log Scale

  15. Summary

  16. Thank You • Paper available on Arxiv • Code available at cs.stanford.edu/~plofgren

  17. Frontier is Important Frontier Aided Significance Thresholding

  18. Algorithm (Simple Version)

  19. Algorithm (Simple Version)

  20. Average Running Time Reverse Work (Local Update) Forward Work (Monte-Carlo)

  21. Correctness

  22. Algorithm (Theoretical Version)

  23. Algorithm (Theoretical Version)

  24. Local Update Algorithm v1 u t v2 U U U v3 U

  25. Local Update Algorithm

  26. Local Update Algorithm

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