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
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Motivation: Personalized Search
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Re-ranked by PPR
Result Preview
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2 sec
6 min1.2 hour
Fast-PPR Monte-Carlo
Local-Update
Personalized PageRank
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Goal
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Previous Algorithm: Monte-Carlo
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Previous Algorithm: Monte-Carlo[Avrachenkov, et al 2007]
Previous Algorithm: Local Update
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[Anderson, et al 2007]
Main Result
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Analogy: Bidirectional Search
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Bidirectional PageRank Algorithm
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Reverse Work(Frontier Discovery)
Forward Work(Random Walks)
u
Main Idea
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Experimental Setup
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Empirical Running Time
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Log Scale
Summary
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Thank You
• Paper available on Arxiv
• Code available at cs.stanford.edu/~plofgren
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Frontier is Important
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FrontierAidedSignificanceThresholding
Algorithm (Simple Version)
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Algorithm (Simple Version)
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Average Running Time
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Reverse Work (Local Update)
Forward Work (Monte-Carlo)
Correctness
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Algorithm (Theoretical Version)
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Algorithm (Theoretical Version)
v1
Local Update Algorithm
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Uu Uv2
Uv3
Ut
Local Update Algorithm
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Local Update Algorithm
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