PPT-More About PageRank

Hubs and Authorities HITS Combatting Web Spam Dealing with NonMainMemory Web Graphs Jeffrey D Ullman Stanford University HITS Hubs Authorities Solving the Implied Recursion 3 Hubs and . 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. . Graph Algorithms. Lin and Dyer’s Chapter 5. Issues in processing a graph in MR. Goal: start from a given node and label all the nodes in the graph so that we can determine the shortest distance. Representation of the graph (of course, generation of a synthetic graph). Hui. Li. Judy . Qiu. Some material adapted from slides by Adam . Kawa. the 3. rd. meeting of WHUG June 21, 2012. What is Pig. Framework for analyzing large un-structured and semi-structured data on top of Hadoop.. Hui. Li. Judy . Qiu. Some material adapted from slides by Adam . Kawa. the 3. rd. meeting of WHUG June 21, 2012. What is Pig. Framework for analyzing large un-structured and semi-structured data on top of Hadoop.. 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.. 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 . Hui. Li. Judy . Qiu. Some material adapted from slides by Adam . Kawa. the 3. rd. meeting of WHUG June 21, 2012. What is Pig. Framework for analyzing large un-structured and semi-structured data on top of Hadoop.. 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. 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. . Information. . Retrieval. . Systems. . as. . a. . Dueling. . Bandits. . Problem. Tingdan. . Luo. tl3xd@virginia.edu. 05/02/2016. Offline. . Learning. . to. . Rank. Goal:. . Finding. . 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.”. 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