PageRank and Similar Ideas - SNAP - Stanford University

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2/12/2012 Jure Leskovec, Stanford C246: Mining Massive Datasets 52
Techniques for achieving high relevance/importance for a web page 1) Term spamming Manipulating the text of web pages in order to appear relevant to queries 2) Link spamming Creating link structures that boost PageRank or Hubs and Authorities scores 2/12/2012 Jure Leskovec, Stanford C246: Mining Massive Datasets 53
Page 1 and 2: CS246: Mining Massive Datasets Jure
Page 3 and 4: Measures generic popularity of a pa
Page 5 and 6: Assume each walker has a small prob
Page 7 and 8: 0.5 0.4 1 0.2 0.5 0.4 2 3 0.8 1 1 1
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: PageRank and HITS are two solutions
Page 55: Analyze text using statistical meth

PageRank and Similar Ideas - SNAP - Stanford University

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