PPT-PageRank Random Surfers on the Web

Transition Matrix of the Web Dead Ends and Spider Traps TopicSpecific PageRank Hubs and Authorities Jeffrey D Ullman Stanford University Intuition 1 Web pages are important if people visit them a lot. Game . R. eview. Basic Information. Company Name. : . Kiloo. Games and . Sybo. . Games. Type of Game: Action (Endless Runner). Price. : Free. Release. . Date. : May 24. th. , 2012. Size. : 36.0 MB. Adapted from UMD Jimmy Lin’s slides, which . is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United . States. See . http://. creativecommons.org. /licenses/by-. nc. -. CS2HS Workshop. Google. Google’s . Pagerank. algorithm is a marvel in terms of its effectiveness and simplicity.. The first company whose initial success was entirely due to “discovery/invention” of a clever algorithm.. Query-independent LAR. Have an a-priori ordering of the web pages. Q. : Set of pages that contain the keywords in the query . q. Present the pages in . Q. ordered according to order . π. What are the advantages of such an approach?. How do search engines decide how to rank your query results?. Guess why Google ranks the query results the way it does. How would you do it?. Naïve ranking of query results. Given query . q. Rank the web pages . Game . R. eview. Basic Information. Company Name. : . Kiloo. Games and . Sybo. . Games. Type of Game: Action (Endless Runner). Price. : Free. Release. . Date. : May 24. th. , 2012. Size. : 36.0 MB. Link Analysis and Web Search. Chapter 14, from D. Easley and J. Kleinberg. Jure . Leskovez. slides CS224W course . Topics. Web Search. HITS. . PageRank. How to Organize the Web. First try. : Human . Link analysis. Instructor: Rada Mihalcea. (Note: This slide set was adapted from an IR course taught by Prof. Chris Manning at Stanford U.). 2. The . Web . as a . Directed . G. raph . Assumption 1. : . cs160. Fall 2009. adapted from:. http://www.stanford.edu/class/cs276/handouts/. lecture15-linkanalysis.ppt. http://webcourse.cs.technion.ac.il/236522/Spring2007/ho/WCFiles/Tutorial05.ppt. Administrative. Data-Intensive Distributed Computing Part 4: Analyzing Graphs (2/2) This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States See http://creativecommons.org/licenses/by-nc-sa/3.0/us/ for details Big Data Infrastructure Week 5: Analyzing Graphs (2/ 2) This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States See http://creativecommons.org/licenses/by-nc-sa/3.0/us/ for details Graph algorithms . A prototypical graph algorithm: PageRank. In memory. Putting more and more on disk …. Sampling from a graph. What is a good sample? (. graph statistics. ). What methods work? (PPR/RWR). “Data is the oil of the new age”. “Data is the oil of the new age”. but, just like oil,. “unrefined data cannot really be used”. “Data is the oil of the new age”. but, just like oil,. William Cohen. Outline. Last week:. PageRank – one sample algorithm on graphs. edges and nodes in memory. nodes in memory. nothing in memory. Aapo. Properties of (social) graphs. GraphLab. /. Powergraph.