DCS/CSCI 2350: Social & Economic Networks WWW: Information - - PDF document

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DCS/CSCI 2350: Social & Economic Networks WWW: Information - - PDF document

5/4/16 DCS/CSCI 2350: Social & Economic Networks WWW: Information Networks Chapters 13, 14 Mohammad T . Irfan Announcements u My office hours u Today 12pm, Tuesday 46pm u Cory's office hours u Wednesday 79pm u


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DCS/CSCI 2350: Social & Economic Networks

WWW: Information Networks Chapters 13, 14

Mohammad T . Irfan

Announcements

u My office hours

u Today 1—2pm, Tuesday 4—6pm

u Cory's office hours

u Wednesday 7—9pm

u Assignment 5 due

u Wednesday 11:59pm

u Writing assignment?

u Due: last day of class

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Questions

  • 1. What does the web look like? [Ch 13]
  • 2. How does Google search it? [Ch 14]

Information network

u Common elements of social and economic

network

u Graphs, paths, giant components u Connections to matching markets and auctions

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Web

u Application for sharing info over the Internet u Created by Tim Berners-Lee (1989—91) u 2 perspectives

u Web pages: Make documents easily available to

anyone on the Internet

u Browser: Retrieve and display documents

u Web organizes information in a unique

fashion

u Different from library system u Different from folders in a computer u Different from indexing

u Hypertext

Hypertext

u Replaces linear structure of text by pointers u Concept dates back to 1950s

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Precursor to hypertext

u Citation network

Precursor to hypertext

u Semantic network

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Precursor to hypertext

u Vannevar Bush (1945)

u Associative memory in “Memex” u Cited by Tim Berners-Lee

Evolution of the web

u Navigational functions (1990s)

u Static web pages

u Transactional functions

u Dynamic, real-time operations

u Web 2.0

u New attitude to technology, not new technology 1.

Collective creation and maintenance of shared content (Wikipedia)

2.

Move personal data to corporate servers (Gmail)

3.

Network among individuals, not just web pages (Facebook)

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Web as a directed graph

u Nodes: Web pages u Directed edges: Links u bowdoin.edu à Restaurants and Lodgings à

Brunswick Downtown Association à Visit à Things to do à Museum of Art à bowdoin.edu

u A directed cycle

Example

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Strongly Connected Component (SCC)

Bow-tie structure of the web

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Link analysis and web search

Chapter 14

Web search

u Google “Bowdoin”

u What do you see? u Why is Bowdoin College ranked first? (Why not

James Bowdoin?) u Google’s source of information is the web

itself

u No expert intervention

u There must be enough information intrinsic

to the web!

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Information retrieval

u 1960s: Search repositories of newspapers,

patents, etc. by keywords

u Done by specialized people

u Challenges in web search

u Synonymy: scallion vs. onion u Polysemy: jaguar (you mean the animal or the car

  • r the football team?)

u Search results must be dynamic u Abundance of information (opposite of needle-in-

haystack)

Ranking algorithms

u Voting by in-links u Hubs and authorities u PageRank

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Voting by in-links

u Highest in-degree node is ranked first, and so

  • n…

Hubs and authorities algorithm (1998)

Image source: http://www.math.cornell.edu/~mec/Winter2009/RalucaRemus/Lecture4/lecture4.html

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Hubs and authorities example

Initially, all hub and auth scores are 1 hub score = 0 auth score auth score = 0 hub score

After round 1

Hubs and authorities example (cont…)

Round 2

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Hubs and authorities example

Normalization

PageRank

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Modern web search

u Google, Bing, (Yahoo!, Ask) u PageRank is a central ingredient of Google

u There are more ingredients

u In 2004, Google incorporated the “Hilltop”

method (2001); Ask incorporated the hubs and authorities algorithm

u Exact search method: secret!

Modern web search

u Combination of links, text, and clicks

u Anchor text: “I’m a student of Bowdoin College.”

u Moving target

u Google’s changes in algorithm causes millions of

dollars of damage to many companies

u Companies seek help from SEOs to climb up the

ranking

u “white hat” vs. “black hat” optimization (later)

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PageRank (PR) (1998)

u Intuition u Update rule u Demo

u NetLogo

JCPenney scandal (2011)

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How JCPenney did it

u Hired SearchDex u Black hat optimization

Image source: http://blogs.cornell.edu/info2040/2011/11/03/j-c-penney%E2%80%99s-pagerank/

How they got caught

u NY Times + Blue Fountain

Media

u Punishment (Feb 9, 2011)

u 7 pm: J. C. Penney was still the

  • No. 1 result for “Samsonite carry
  • n luggage.”

u 9 pm: It was at No. 71. u Similar with other keywords

u Precedence: BMW in Germany

(2006)

Google’s spam cop Matt Cutts

(Image source: NY Times)

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Link analysis beyond web search

u Citation analysis

u Journal’s impact scores

Link analysis of U.S. Supreme Court citations

u Fowler & Jeon’s study (2008) u Hubs and authorities algorithm applied to

data spanning 2 centuries!

u Important precedence has very high

authority score

u Public recognition comes later (authority

score can predict future popularity)

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Link analysis of U.S. Supreme Court citations

u Rise and fall of authority scores

Case study: Bing vs. Google