PPT-Local Computation of PageRank Contributions

Reid Andersen Christian Borgs Jennifer Chayes John Hopcraft Vahab S Mirrokni and Shang Hua Teng Omer Rotem Introduction PageRank The web as a graph each website is a vertex a . 1 and 1722A 1 et seq Unlimited Unlimi ted Unlimited 500candidateelection aj Unlimited Alaska 57511 1513065 to 080 500candidateyear Aggregate amounts candidates may accept from nonresidents 20000yeargub candidate 5000yearsenate candidate 3000yearhouse PAGE RANK (determines the importance of webpages based on link structure). Solves a complex system of score equations. PageRank is a . probability distribution. used to represent the likelihood that a person randomly clicking on links will arrive at any particular page. . 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?. PAGE RANK (determines the importance of webpages based on link structure). Solves a complex system of score equations. PageRank is a . probability distribution. used to represent the likelihood that a person randomly clicking on links will arrive at any particular page. . NIbble. 2. Why I’m talking about graphs. Lots of large data . is . graphs. Facebook, Twitter, citation data, and other . social. networks. The web, the blogosphere, the semantic web, Freebase, . W. 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 . Adrian Farrel. Old Dog Consulting. adrian@olddog.co.uk. History of PCE. We know where PCE comes from. Simple CSPF computation of paths for MPLS-TE. But RFC 4655 was not quite so limited in its definition. 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. : . Ashish Goel. Joint work with Peter Lofgren; Sid Banerjee; C . Seshadhri. 1. Personalized PageRank. 2. Assume a directed graph with . n. nodes and . m. edges. Motivation: Personalized Search. . 3. Motivation: Personalized Search. 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). Active contributions to computation. Dendrites as computational elements:. Examples. Dendritic. computation. r. V. m. = . I. m. . R. m. Current flows uniformly out through the cell: . I. m. = . I. 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.