PPT-Ensemble Enabled Weighted PageRank

Dongsheng Luo Chen Gong Renjun Hu Liang Duan Shuai Ma Niannian Wu Xuelian Lin TeamBUAA Problem amp Challenges Problem rank nodes in a heterogeneous graph based on queryindependent node importance . for the NCEP GFS. Tom Hamill, for . Jeff . Whitaker. NOAA Earth System Research Lab, Boulder, CO, USA. jeffrey.s.whitaker@noaa.gov. Daryl Kleist, Dave Parrish and John . Derber. National Centers for Environmental Prediction, Camp Springs, MD, USA. Boosting, Bagging, Random Forests and More. Yisong Yue. Supervised Learning. Goal:. learn predictor h(x) . High accuracy (low error). Using training data {(x. 1. ,y. 1. ),…,(. x. n. ,y. n. )}. Person. Ensemble Clustering. unlabeled . data. ……. F. inal . partition. clustering algorithm 1. combine. clustering algorithm . N. ……. clustering algorithm 2. Combine multiple partitions of . given. data . 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). LOTTERIES.  . SEA Webinar Series: . Weighted Lotteries . Implementing Weighted Lotteries. Colorado Department of Education . Gina . Schlieman. , Charter School Program and Grant Manager. Colorado Context. 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.. Greatly Improved Protein Folding Statistics. Using WorkQueue and Condor. Jeff Kinnison & Dr. Jesus A. Izaguirre. Studying a New Protein. HP24stab. Subdomain of the Villin headpiece. Two-helical supersecondary structure. Bright, . Colle. , . DiMego. , Hacker, Whitaker. 22 Aug. 2012. DTC SAB ensemble task. 1. Primary recommendation. Continue to pursue long-term goal of pivotal and more tangible role in research-to-operations (R2O) transitions. . 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. May. . 2013. General presentation. 0 1 0 0 1 1 0 0 1 0 0 1 1 0 1 0 1 0 1 0 0 1 0 1 0 1 1 1 1 0 0 0 1 0 1 0 1 0 1 0. 0 1 1 0 0 0 1 1 1 0 0 1 0 1 0 1 0 1 0 1 1 0 1 0 1 0 1 1 1 1 0 0 0 1 1 0 1 0 1 0. Keith Dalbey, PhD. Sandia National Labs, Dept 1441. Optimization & Uncertainty Quantification. Abani. K. . Patra. , PhD. Department of Mechanical & Aerospace Engineering, University at Buffalo. 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.”.