1 / 9

Part I: Web Structure Mining Chapter 2: Hyperlink Based Ranking

Part I: Web Structure Mining Chapter 2: Hyperlink Based Ranking. Social Network Analysis PageRank Authorities and Hubs Link Based Similarity Search Enhanced Techniques for Page Ranking. Social Networks. Directed graph with weights assigned to its edges

hue
Download Presentation

Part I: Web Structure Mining Chapter 2: Hyperlink Based Ranking

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Part I: Web Structure MiningChapter 2: Hyperlink Based Ranking • Social Network Analysis • PageRank • Authorities and Hubs • Link Based Similarity Search • Enhanced Techniques for Page Ranking Zdravko Markov and Daniel T. Larose, Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage, Wiley, 2007. Slides for Chapter 1: Information Retrieval an Web Search

  2. Social Networks • Directed graph with weights assigned to its edges • Nodes represent documents and the edges – citations from one document to other documents. • Prestige can be associated with the number of input edges to a node (in-degree). • Prestige has a recursive nature. depends on the authority (or again, the prestige) of citations Zdravko Markov and Daniel T. Larose, Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage, Wiley, 2007. Slides for Chapter 1: Information Retrieval an Web Search

  3. Social Networks • adjacency matrix • if document cites document • otherwise • prestige score Zdravko Markov and Daniel T. Larose, Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage, Wiley, 2007. Slides for Chapter 1: Information Retrieval an Web Search

  4. Social Networks • Computing prestige • Eigen decomposition • EigenvectorP • Eigenvalue Zdravko Markov and Daniel T. Larose, Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage, Wiley, 2007. Slides for Chapter 1: Information Retrieval an Web Search

  5. Social Networks Zdravko Markov and Daniel T. Larose, Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage, Wiley, 2007. Slides for Chapter 1: Information Retrieval an Web Search

  6. Social Networks Power Iteration • Loop: • While Zdravko Markov and Daniel T. Larose, Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage, Wiley, 2007. Slides for Chapter 1: Information Retrieval an Web Search

  7. PageRank • “Random web surfer”keeps clicking on hyperlinks at random with uniform probability • Implements random walk on the web graph • Page u links to web pages • Probability of visiting page v will be • Amount of prestige that page v receives from page u is of the prestige of u Zdravko Markov and Daniel T. Larose, Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage, Wiley, 2007. Slides for Chapter 1: Information Retrieval an Web Search

  8. PageRank Propagation of page rank Zdravko Markov and Daniel T. Larose, Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage, Wiley, 2007. Slides for Chapter 1: Information Retrieval an Web Search

  9. PageRank Calculation of page rank Norm Norm Integers Zdravko Markov and Daniel T. Larose, Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage, Wiley, 2007. Slides for Chapter 1: Information Retrieval an Web Search

More Related