PPT-Analysis of Large Graphs:

Link Analysis PageRank Mining of Massive Datasets Jure Leskovec Anand Rajaraman Jeff Ullman Stanford University httpwwwmmdsorg Note to other teachers and users of these slides. There is actually only one main program the spe ci64257c layout algorithms implemented as plugins Thus the yl ar gely share all of the same commandline options dot draws directed graphs It works well on D AG sa nd other graphs that can be drawn as h Anthony Bonato. Ryerson University. CanaDAM. 2011. Cop number of a graph. the . cop number of a graph. , written . c(G). , is an elusive graph parameter. few connections to other graph parameters. hard to compute. Dr. Andrew Wallace PhD . BEng. (hons) . EurIng. andrew.wallace@cs.umu.se. Overview. Sets. Implementation. Complexity. Graphs. Constructing . Graphs. Graph examples. Sets. Collection of items. No specified ordered. Isabelle Stanton, UC Berkeley. Gabriel . Kliot. , Microsoft Research XCG. Modern graph datasets are huge. The web graph had over a trillion links in 2011. Now?. . facebook. has “more than 901 million users with average degree 130”. 2-3. In addition to the histogram, the frequency polygon, and the . ogive. , several other types of graphs are often used in statistics. They are the bar graph, Pareto chart, time series graph, and pie chart. . Learning Goals:. Graphs of the Cosecant, Secant, and Cotangent Functions. Graph transformations . When you think about the . csc. , sec, and cot graphs what do you think about?. Graph of the Cosecant Function. Masaru . Kamada. Tokyo . University of . Science. Graph Theory Conference. i. n honor of Yoshimi . Egawa. on the occasion his 60. th. birthday. September 10-14, 2013. In this talk, all graphs are finite, undirected and allowed multiple edges without loops.. Daniel A. Spielman. Yale University. AMS Josiah Willard Gibbs Lecture. January . 6. , 2016 . From Applied to Pure Mathematics. Algebraic and Spectral Graph Theory. . . Sparsification. :. a. pproximating graphs by graphs with fewer edges. Misleading Graphs. Good . graphs are extremely powerful tools for displaying large quantities . of complex . data; they help turn the realms of information available today . into knowledge. . But, unfortunately, some graphs deceive or mislead. This . Minors, . Bidimensionality. ,. & Decomposition. r. r. Erik Demaine. MIT. Goals. How far . beyond planar graphs . can we go?. Graphs excluding. a fixed minor. Powers thereof. Build . general approximation frameworks . Daniel A. Spielman. Yale University. AMS Josiah Willard Gibbs Lecture. January . 6. , 2016 . From Applied to Pure Mathematics. Algebraic and Spectral Graph Theory. . . Sparsification. :. a. pproximating graphs by graphs with fewer edges. Instructions – using H7. Read through the . first . 4 . slides to understand how graphs can be used to misrepresent data, or at least try to mislead you.. On a new slide explain CLEARLY how the next . The vertical scale is too big or too small, or skips numbers, or doesn’t start at zero.. The graph isn’t labeled properly.. Data is left out.. But some real life misleading graphs go above and beyond the classic types. Some are intended to mislead, others are intended to shock. And in some cases, well-meaning individuals just got it all plain wrong. These are some of my favorite recent-history misleading graphs from real life.. Lingxiao Ma. . †. , Zhi Yang. . †. , Youshan Miao. ‡. , Jilong Xue. ‡. , Ming Wu. ‡. , Lidong Zhou. ‡. , . Yafei. Dai. . †. †. . Peking University. ‡ . Microsoft Research. USENIX ATC ’19, Renton, WA, USA.