fast-ppr: personalized pagerank estimation for large graphs
DESCRIPTION
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. - PowerPoint PPT PresentationTRANSCRIPT
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FAST-PPR: Personalized PageRank Estimation for Large Graphs
Peter Lofgren (Stanford)Joint work with Siddhartha Banerjee (Stanford), Ashish Goel (Stanford), and C. Seshadhri (Sandia)
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Motivation: Personalized Search
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Motivation: Personalized SearchRe-ranked by PPR
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Result Preview
2 sec
6 min1.2 hour
Fast-PPR Monte-Carlo
Local-Update
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Personalized PageRank
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Goal
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Previous Algorithm: Monte-CarloPrevious Algorithm: Monte-Carlo[Avrachenkov, et al 2007]
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Previous Algorithm: Local Update[Anderson, et al 2007]
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Main Result
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Analogy: Bidirectional Search
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Bidirectional PageRank Algorithm
Reverse Work(Frontier Discovery)
Forward Work(Random Walks)
u
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Main Idea
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Experimental Setup
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Empirical Running Time
Log Scale
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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
FrontierAidedSignificanceThresholding
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Algorithm (Simple Version)
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Algorithm (Simple Version)
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Average Running Time
Reverse Work (Local Update)
Forward Work (Monte-Carlo)
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Correctness
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Algorithm (Theoretical Version)
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Algorithm (Theoretical Version)
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v1
Local Update Algorithm
Uu Uv2
Uv3
Ut
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Local Update Algorithm
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Local Update Algorithm