PPT-Geodesic graphs Yunus Akkaya

Rommy Marquez Heather Urban Marlana Young Definitions G VE V the set of all vertices in G EXAMPLE VABCD E the set of all edges in G EXAMPLE EAB AC BC BD CD. 16 N. H. Abdel-All and E. I. Abdel-Galil 1.Introduction Geodesics are curves on a surface that make turns just to stay on Sometimes, two graphs have exactly the same form, in the sense that there is a one-to-one correspondence between their vertex sets that preserves edges. In such a case, we say that the two graphs are . for Unsupervised Domain Adaptation. Boqing. Gong . University . of Southern . California. Joint work with Yuan . Shi, . Fei. . Sha. , and Kristen . Grauman. . 1. Motivation. Mismatch. Network Science: Random Graphs . 2012. Prof. Albert-László Barabási. Dr. Baruch Barzel, Dr. Mauro Martino. RANDOM NETWORK MODEL. Network Science: Random Graphs . 2012. Erdös-Rényi model (1960). 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. . 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.. 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 . 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 . Section . 10.3. Representing Graphs: . Adjacency Lists. Definition. : An . adjacency list . can be used to represent a graph with no multiple edges by specifying the vertices that are adjacent to each vertex of the graph.. Some pages are adapted. from Dan Ryan, . Mills College. B. etweenness. Centrality. I. ntuition. : how many pairs of individuals would have to go through you in order to reach one another in the minimum number of hops. Ralucca Gera,. Applied Mathematics Dept.. Naval Postgraduate School. Monterey, California. rgera@nps.edu. Different types of centralities:. 2. Source: Discovering Sets of Key Players in Social Networks – Daniel Ortiz-Arroyo – Springer 2010/. moduli. spaces of curves. . Feng. Luo. Rutgers University. Joint work with Ser-. Peow. Tan. .