PPT-Graphs

What is a graph In simple words A graph is a set of vertices and edges which connect them A node or vertex is a discrete position in the graph An edge or connection is a link between two vertices. 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. 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 . 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. 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). L. á. szl. ó. . Lov. á. sz. Eötvös Loránd University. Budapest . September 2012. 1. September 2012. Tur. á. n’s Theorem . (special case proved by Mantel):. . G. contains no triangles .  #edges. 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. Anthony Bonato. Ryerson University. East Coast Combinatorics Conference. co-author. talk. post-doc. Into the infinite. R. Infinite random geometric graphs. 111. 110. 101. 011. 100. 010. 001. 000. Some properties. Unit 1 – Introduction to Physics. Vocabulary . Symbols equation graph. word equations variables x-axis. y-axis dependent variable tangent. Independent variable gradient. y-intercept x-intercept delta (. Graphs.  . Graphs . capture . much more detail than numerical summaries, so very useful for learning about data and communicating its features.. At the same time, graphical interpretation isn’t standard in the way that numerical summaries are, and our eyes can fool us.. 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 type of graph you draw depends on the types of observations you make. Bar Graph. Line Graph. Pie Graph. Bar and Column Graphs. Bar and column graphs. Some observations fall into . discrete. groupings. 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.. Planar graphs. 2. Planar graphs. Can be drawn on the plane without crossings. Plane graph: planar graph, given together with an embedding in the plane. Many applications…. Questions:. Testing if a graph is planar. Graphs and Graph Models. Graph Terminology and Special Types of Graphs. Representing Graphs and Graph Isomorphism. Connectivity. Euler and Hamiltonian Paths. Graphs and Graph Models. Section . 10.1. Section Summary.