PageRank and Similar Ideas - SNAP - Stanford University

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Let: A ij = M ij + (1‐) /|S| if iS M ij otherwise A is stochastic! We have weighted all pages in the teleport set S equally Could also assign different weights to pages! Compute as for regular PageRank: Multiply by M, then add a vector Maintains sparseness 2/12/2012 Jure Leskovec, Stanford C246: Mining Massive Datasets 6
0.5 0.4 1 0.2 0.5 0.4 2 3 0.8 1 1 1 0.8 0.8 4 Suppose S = {1}, = 0.8 Node Iteration 0 1 2… stable 1 1.0 0.2 0.52 0.294 2 0 0.4 0.08 0.118 3 0 0.4 0.08 0.327 4 0 0 0.32 0.261 Note how we initialize the PageRank vector differently from the unbiased PageRank case. 2/12/2012 Jure Leskovec, Stanford C246: Mining Massive Datasets 7
Page 1 and 2: CS246: Mining Massive Datasets Jure
Page 3 and 4: Measures generic popularity of a pa
Page 5: Assume each walker has a small prob
Page 10 and 11: Spamming: any deliberate action to
Page 12 and 13: As people began to use search engin
Page 14 and 15: Believe what people say about you,
Page 16 and 17: Once Google became the dominant sea
Page 18 and 19: Spammer’s goal: Maximize the Pag
Page 20 and 21: x: PageRank contributed by accessib
Page 23 and 24: Combating term spam Analyze text u
Page 25 and 26: Call the subset of seed pages that
Page 27 and 28: Trust attenuation: The degree of t
Page 29 and 30: Suppose we want to pick a seed set
Page 31: (p) = PageRank of page p r + (p) =
Page 34 and 35: … IJCAI KDD ICDM SDM AAAI NIPS
Page 37 and 38: HITS (Hypertext‐Induced Topic Sel
Page 39 and 40: Interesting pages fall into two cla
Page 41 and 42: Hubs collect authority scores (Note
Page 43 and 44: A good hub links to many good autho
Page 45 and 46: HITS converges to a single stable p
Page 47 and 48: The HITS algorithm: Initialize h,
Page 49 and 50: HITS algorithm in new notation: Se
Page 51 and 52: PageRank and HITS are two solutions
Page 53 and 54: Techniques for achieving high relev
Page 55: Analyze text using statistical meth

PageRank and Similar Ideas - SNAP - Stanford University

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