PPT-Subsampling Graphs 1 RECAP of PageRank-

NIbble 2 Why Im 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. 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). on . Evolving Graphs Research. Speaker: . Chenghui. . Ren. Supervisors: Prof. Ben Kao, . Prof. . David Cheung. 1. Motivation. Evolving graphs are everywhere. Social networks. Users join social networks. 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.. Link . Analysis, PageRank. Mining of Massive Datasets. Jure Leskovec, . Anand. . Rajaraman. , Jeff Ullman . Stanford University. http://www.mmds.org . Note to other teachers and users of these . slides:. 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 . lecture capture. Phil Ansell (Maths & Stats). Carol Summerside (. QuILT. ). . Tom Nye (Maths & Stats). Andrew Lovatt (EEE). Nick Randall (GPS). 2. nd. July 2012. Outline. Introductions and Background. 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.”. Updates and Data Analysis. Zack Lane. ReCAP. Coordinator. January 24, 2012. ReCAP. Columbia University . ReCAP. Columbia University . Zack’s Ulterior Motives. Excuse to . share. Find forum to share, examine, . 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 person. grass. trees. motorbike. road. Evaluation metric. Pixel classification!. Accuracy?. Heavily unbalanced. Common classes are over-emphasized. Intersection over Union. Average across classes and images. Always start off with a joke, to lighten the mood. 1. The course so far. Cost of operations. Streaming learning algorithms. Parallel streaming with map-reduce. Building complex parallel algorithms with dataflow languages (Pig, .