lect17

lect17 - More on Rankings Query-independent LAR Have an...

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More on Rankings

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Query-independent LAR Have an a-priori ordering of the web pages Q : Set of pages that contain the keywords in the query q Present the pages in Q ordered according to order π What are the advantages of such an approach?
InDegree algorithm Rank pages according to in-degree w i = |B(i)| 1. Red Page 1. Yellow Page 1. Blue Page 1. Purple Page 1. Green Page w=1 w=1 w=2 w=3 w=2

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PageRank algorithm [BP98] Good authorities should be pointed by good authorities Random walk on the web graph pick a page at random with probability 1- α jump to a random page with probability α follow a random outgoing link Rank according to the stationary distribution 1. Red Page 1. Purple Page   1. Yellow Page 1. Blue Page 1. Green Page ( 29 n q F q PR p PR p q 1 1 ) ( ) ( ) ( α α - + =
Markov chains A Markov chain describes a discrete time stochastic process over a set of states according to a transition probability matrix

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Random walks Random walks on graphs correspond to Markov Chains The set of states S is the set of nodes of the graph G The transition probability matrix is the probability that we follow an edge from one node to another
An example v 1 v 2 v 3 v 4 v 5 = 2 1 0 0 0 2 1 0 0 3 1 3 1 3 1 0 0 0 1 0 1 0 0 0 0 0 0 2 1 2 1 0 P = 1 0 0 0 1 0 0 1 1 1 0 0 0 1 0 1 0 0 0 0 0 0 1 1 0 A

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State probability vector
An example = 0 2 1 0 0 2 1 0 0 3 1 3 1 3 1 0 0 0 1 0 1 0 0 0 0 0 0 2 1 2 1 0 P v 1 v 2 v 3 v 4 v 5 q = 1/3 q + 1/2 q t 5 q = 1/2 q t 5

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Stationary distribution
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