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 . In this paper we present an algorithm for local graph partitioning using personalized PageRank vectors We develop an improved algorithm for computing approximate PageRank vectors and derive a mixing result for PageRank vectors similar to that for ra 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 with . Graph Computation. Joseph Gonzalez. jegonzal@cs.cmu.edu. Download the talk: . http://tinyurl.com/7tojdmw. http://www.cs.cmu.edu/~jegonzal/talks/biglearning_with_graphs.pptx. 48 Hours of Video . Jessica Clark, . Buffalo NCF Chairperson. Sylvia Karpf, Associate Director of Special Programs and Services. Potential Contributors. Local Banks. Any organization that provides a free service or resource to students as they transition from high school to college. Min Huang. (. min.huang@jpl.nasa.gov. ), . K. W. Bowman (JPL), G. R. Carmichael (U Iowa), . M. Lee (JPL), D. K. . Henze. (CU-Boulder), T. Chai (NOAA/ARL) . A. J. . Weinheimer. (NCAR), R. C. Cohen (UC Berkeley). 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?. Dongsheng. Luo, Chen Gong, . Renjun. Hu. , Liang . Duan. Shuai. Ma, . Niannian. Wu, . Xuelian. Lin. TeamBUAA. Problem & Challenges. Problem: . rank nodes in a heterogeneous graph based on query-independent node importance . Hubs and Authorities (HITS). Combatting Web Spam. Dealing with Non-Main-Memory Web Graphs. Jeffrey D. Ullman. Stanford University. HITS. Hubs. Authorities. Solving the Implied Recursion. 3. Hubs and . 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. 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. Search Engines And Ranking Algorithms. “The first-ever World Wide Web site went online in 1991, although this doesn’t seem that long ago, it is hard to imagine the world before Sir Tim Berners-Lee’s invention. In many ways, the colossal impact of the World Wide Web is obvious. Many people, however, may not fully appreciate the underlying technical contributions that make the Web possible. Sir Tim Berners-Lee not only developed the key components, such as URIs and web browsers that allow us to use the Web, but offered a coherent vision of how each of these elements would work together as part of an integrated whole.”. 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.