PageRank and Similar Ideas - SNAP - Stanford University

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Hubs and Authorities Each page has 2 scores: Quality as an expert (hub): Total sum of votes of pages pointed to Quality as an content (authority): Total sum of votes of experts Principle of repeated improvement NYT: 10 Ebay: 3 Yahoo: 3 CNN: 8 WSJ: 9 2/12/2012 Jure Leskovec, Stanford C246: Mining Massive Datasets 38
Interesting pages fall into two classes: 1. Authorities are pages containing useful information Newspaper home pages Course home pages Home pages of auto manufacturers 2. Hubs are pages that link to authorities List of newspapers Course bulletin List of US auto manufacturers NYT: 10 Ebay: 3 Yahoo: 3 CNN: 8 WSJ: 9 2/12/2012 Jure Leskovec, Stanford C246: Mining Massive Datasets 39
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: HITS (Hypertext‐Induced Topic Sel
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

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