CSE 450

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CSE 450 Web Mining SeminarProfessor Brian D.
DavisonFall 2005

• A PRESENTATION on
• What is this Page Known for?
• Computing Web Page Reputations
• D. Rafiei A.O. Mendelzon
• WWW9 Conference, Amsterdam, May 2000
• by
• Osama Ahmed Khan

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OVERVIEW

• Introduction
• Motivation
• Random Walks On The Web Graph
• One-level Influence Propagation
• Two-level Influence Propagation
• Issues
• Evaluation
• Limitations

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Introduction

• To find a ranked set of pages which have a
  reputation on a topic
• To find a ranked set of topics on which a webpage
  has a reputation
• Reputation Evaluated on the basis of
• Navigation
• Subsumption
• Relatedness
• Refutation
• Justification

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Motivation

• Organizational Review
• Page Classification
• Personal Review

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Random Walks On The Web Graph

• Given a set S s1, s2, . , sn) of states,
• Random Walk
• Switches
• Stays

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One-level Influence Propagation

• Random Surfer
• Selects a page
• Follows a link
• Reputation
• Total number of visits

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One-level Model

• Reputation
• Probability that random surfer looking for topic
  t will visit page p at step n of the walk

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One-level Model (Contd.)

• One-level Reputation Rank
• Equilibrium Probability of visiting page p for
  topic t

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Two-level Influence Propagation

• Random Surfer
• Selects a page
• Follows a link
• Forward
• Backward
• Reputation
• Authority Total number of Forward visits
• Hub Total number of Backward visits

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Two-level Model

• Reputation
• Authority Probability that random surfer looking
  for topic t makes a forward visit to page p
  at step n of the walk

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Two-level Model (Contd.)

• Reputation (Contd.)
• Hub Probability that random surfer looking for
  topic t makes a backward visit to page p at
  step n of the walk

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Two-level Model (Contd.)

• Two-level Reputation Rank
• Equilibrium Probability of visiting page p for
  topic t in direction associated to r

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Issues

• Access to large crawl of Web Computation
• Set of pages where ranks are computed
• Algorithm 1 (One-level)
• Algorithm 2 (Two-level)
• Set of topics on which ranks are computed
• Algorithm 1 (One-level)
• Algorithm 2 (Two-level)

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Issues (Contd.)

• No access to large crawl of Web Approximation
• Set of pages where ranks are computed
• Generalization of PageRank Model (One-level)
• Generalization of Hubs and Authorities Model
  (Two-level)
• Set of topics on which ranks are computed
• Algorithm 3 (One-level)
• Algorithm 4 (Two-level)

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Evaluation

• Not access to large crawl of Web Approximation
• Set of topics on which ranks are computed
• Algorithm 3 (One-level)
• Algorithm 4 (Two-level)

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Evaluation (Contd.)

1. Known Authoritative Pages

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Evaluation (Contd.)

1. Personal Home Pages

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Evaluation (Contd.)

1. Unregulated Websites

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Limitations

• Topic representation on Web
• Page connectivity

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• Thank You