PPT-9.3 Representing Graphs and Graph Isomorphism

Sometimes two graphs have exactly the same form in the sense that there is a onetoone correspondence between their vertex sets that preserves edges In such a case we say that the two graphs are . 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 . Lasserre. Gaps,. and Asymmetry of Random Graphs. Ryan O’Donnell (CMU). John Wright (CMU). Chenggang. Wu (. Tsinghua. ). Yuan Zhou (CMU). Hardness of . Robust Graph Isomorphism. ,. . Lasserre. Gaps,. 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. Using the Ullman Algorithm for Graphical Matching. Iddo. . Aviram. OCR- a Brief Review. Optical character recognition. , usually abbreviated to . OCR. , is the mechanical or electronic translation of scanned images of handwritten, typewritten or printed text into machine-encoded text. 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. . Arijit Khan, . Yinghui. Wu, Xifeng Yan. Department of Computer Science. University of California, Santa Barbara. {. arijitkhan. , . yinghui. , . xyan. }@. cs.ucsb.edu. Graph Data. 2. Graphs are everywhere.. 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 . Thanks to Mr. Hammond @ . www.mrhammond.org/math/mathlessons/7-8.ppt. Review. We have been looking at many different ways to present data. . Now let’s see how people can use these charts and graphs to mislead you.. Distance/Time. Distance. Distance. Distance. D. t. D. t. D. t. Gradient = . speed. Distance-Time graph. gradient = velocity. Graphs Representing Motion. Velocity-time graph. Gradient = acceleration. Area . Graph Isomorphism. 2. Today. Graph isomorphism: definition. Complexity: isomorphism completeness. The refinement heuristic. Isomorphism for trees. Rooted trees. Unrooted trees. Graph Isomorphism. 3. Graph Isomorphism. Sultan Almuhammadi ICS 254: Graphs and Trees 1 Graph & Trees Chapters 10-11 Acknowledgement This is a modified version of Module#22 on Graph Theory by Michael Frank Sultan Almuhammadi ICS 254: Graphs and Trees September 9-10, 2014. What do we already know about graphing motion?. We know how to draw and interpret position vs time (P-T) graphs. Straight lines = . . Positive slope Negative slope: Zero slope: . Lasserre. Gaps,. and Asymmetry of Random Graphs. Ryan O’Donnell (CMU). John Wright (CMU). Chenggang. Wu (. Tsinghua. ). Yuan Zhou (CMU). Hardness of . Robust Graph Isomorphism. ,. . Lasserre. Gaps,. Discrete Structures (CS 173). Madhusudan Parthasarathy, University of Illinois. 1. http://en.wikipedia.org/wiki/File:7_bridgesID.png. Last Lecture: Graphs. How to represent graphs?. What are the properties of a graph?.