'Web graph' presentation slideshows

Scalability and Efficiency Challenges in Large-Scale Web Search Engines

1.96k views • 176 slides

Online Social Networks and Media

Online Social Networks and Media . Link Analysis and Web Search. How to Organize the Web. First try : Human curated Web directories Yahoo, DMOZ, LookSmart. How to organize the web. Second try : Web Search Information Retrieval investigates:

765 views • 67 slides

Antonio Gulli

745 views • 55 slides

Link Analysis Ranking

727 views • 51 slides

Link Analysis Ranking

663 views • 51 slides

A Random-Surfer Web-Graph Model

A Random-Surfer Web-Graph Model. (Joint work with Avrim Blum & Hubert Chan ). Mugizi Rwebangira. links.html. index.html. http://cnn.com. resume.html. The Web as a Graph. Consider the World Wide Web as a graph, with web pages as nodes and hyperlinks between pages as edges.

735 views • 51 slides

The influence of search engines on preferential attachment

The influence of search engines on preferential attachment. Dan Li CS3150 Spring 2006. The paper. The influence of search engines on preferential attachment Soumen Chakrabarti, Alan Frieze and Juan Vera. Background. The evolution of social networks through time Web graph Models

603 views • 43 slides

Web search engines

467 views • 40 slides

CS345 Data Mining

441 views • 32 slides

Chapter 5: Link Analysis for Authority Scoring

Chapter 5: Link Analysis for Authority Scoring. 5.1 PageRank (S. Brin and L. Page 1997/1998) 5.2 HITS (J. Kleinberg 1997/1999) 5.3 Comparison and Extensions 5.4 Topic-specific and Personalized PageRank 5.5 Efficiency Issues 5.6 Online Page Importance 5.7 Spam-Resilient Authority Scoring.

398 views • 30 slides

Random Sampling Algorithms with Applications

Aug 25 2010 ERC Workshop. Random Sampling Algorithms with Applications. Kyomin Jung KAIST. Contents. Randomized Algorithm & Random Sampling Application Markov Chain & Stationary Distribution Markov Chain Monte Carlo method Google's page rank. Randomized Algorithm.

403 views • 27 slides

Introduction to Web Science

373 views • 24 slides

Introduction to Scale Free (SF) network

Introduction to Scale Free (SF) network. The Topology of the Internet. by Chan Chi Yuk. Agenda. Motivation Background Scale Free Models Power Laws Summary. Motivation. Want to solve network traffic problem ? Need to know the topology The Internet has done a great job ? But how?.

350 views • 24 slides

Study of Page Rank and HITS

Study of Page Rank and HITS. By: Ankit Sethi FSU. How do Search Engines work. Motive . Goals of Study: Getting aware about Page Rank and HITS- two popular algorithms used by search engines to rank webpages.

369 views • 16 slides

Projects (2012-2013)

Projects (2012-2013). Ida Mele. Rules. Students have to work in teams (max 2 people). The project has to be delivered by the deadline that will be published on my web site.

252 views • 16 slides

Web Taxonomies Discovering the Structure of Information

Web Taxonomies Discovering the Structure of Information. Tim Weninger. Department of Computer Science University of Illinois Urbana-Champaign, Urbana, IL. Information wants to be free. World Wide Web is decentralized and messy. (but it wants to be structured)

243 views • 13 slides

CONTENT AND LINK ANALYSIS FOR SEARCHING THE WEB

CONTENT AND LINK ANALYSIS FOR SEARCHING THE WEB. Kemal Efe, Vijay Raghavan, and Arun Lakhotia University of Louisiana Presented by Lan Nie 0 9 / 0 1/2005, Lehigh University. Introduction Search engine Crawl, index and retrieve information about web pages. find all of the relevant pages

182 views • 9 slides

Problem Addressed

Problem Addressed. Attempts to prove that Web Crawl is random & biased image of Web Graph and does not assert properties of Web Graph Understanding the Hyperlink structure has allowed the design of Algorithms like PageRank, HITS etc. Approach.

137 views • 5 slides

Week 2 - Complex Networks and their Properties

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Ch19. Web search basics

Ch19. Web search basics. 2010. 3. 22. ? ? ?. contents. 19.1 Background and history 19.2 Web characteristics 19.2.1 The web graph 19.2.2 Spam 19.3 Advertising as the economic model 19.4 User query needs 19.5 Index size and estimation 19.6 Near-duplicates and shingling.

359 views • 0 slides