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 SearchRe-ranked by PPR

4

Result Preview

2 sec

6 min1.2 hour

Fast-PPR Monte-Carlo

Local-Update

5

Personalized PageRank

6

Goal

7

Previous Algorithm: Monte-CarloPrevious 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

Reverse Work(Frontier Discovery)

Forward Work(Random Walks)

u

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

18

Frontier is Important

FrontierAidedSignificanceThresholding

19

Algorithm (Simple Version)

20

Algorithm (Simple Version)

21

Average Running Time

Reverse Work (Local Update)

Forward Work (Monte-Carlo)

22

Correctness

23

Algorithm (Theoretical Version)

24

Algorithm (Theoretical Version)

25

v1

Local Update Algorithm

Uu Uv2

Uv3

Ut

26

Local Update Algorithm

27

Local Update Algorithm