PPT-On the Density of a Graph

and its Blowup Raphael Yuster Joint work with Asaf Shapira 2 Formulation and definitions How does the number of copies of one graph H in a graph G affect the number of copies of another graph . Principles Density The density pronounced rho of an object is the ratio of its mass to its volume Since very few substances have exactly the same density this property can be used to identify an element or compound Buoyant Force When you are swim Density is the mass per unit volume of an object. Density = . mass. D = . m. Volume V. Density = love. Mass and volume are extensive properties. These values change depending on how much material you have.. Continuous Random Variables. Dr J Frost (jfrost@tiffin.kingston.sch.uk). www.drfrostmaths.com . Last modified: . 1. st. October 2015. Discrete vs Continuous Distributions. You all know the distinction between discrete and continuous variables:. Representing . Data. Why use a Histogram. When there is a lot of data. When data is . Continuous . a mass, height, volume, time etc. Presented in a Grouped Frequency Distribution. Often. . in groups or classes that are UNEQUAL . What is . denser. a pound of feathers or a pound of bricks. ?. Complete . Comparing Cubes. OPENER . Complete . Comparing Cubes. It Sinks, It Floats. Controls, Variables, Graphing skills. Content Objectives. A graph is a pictorial representation of information recorded in a data table. It is used to show a relationship between two or more factors.. Types of Graphs. Line graphs. are best for displaying data that changes. It best shows the relationship between two variables. . David S. Bindel. Cornell University. ABSTRACT. Most spectral graph theory: . extremal. eigenvalues . and associated eigenvectors.. Spectral geometry, material science: also eigenvalue . distributions. learn 2 + 2 = 10 or something. —. Dennis Rodman. Chemistry Math With Chemistry-specific graphs. Variables. Independent. Variable “I” change. Graphed on “. x. ” axis . Dependent. Variable which “Depends” on independent variable. Introduction. Labelled data. Unlabeled data. cat. dog. (Image of cats and dogs without labeling). Introduction. Supervised learning: . E.g. . : image, . : class. . labels. Semi-supervised learning: . ?. A large, dense population or . a small, scattered population. A small, scattered population. Two . ways a population can decrease in size?. decreased birthrate and emigration. Suppose that a species of . I Density(D). An object’s mass compared to its volume. On Earth we can sometimes use weight for mass.. IV Density Facts. Things with . HIGH Density. : Bowling Ball Shot put. Big Marble. Things with . I Density(D). An object’s mass compared to its volume. On Earth we can sometimes use weight for mass.. IV Density Facts. Things with . HIGH Density. : Bowling Ball Shot put. Big Marble. Things with . High Density Clusters June 2017 1 Idea Shift Density-Based Clustering VS Center-Based. 2 Main Objective Objective: find a clustering of tight knit groups in G. 3 Clustering Algorithm : Recursive Algorithm based on Sparse Cuts Chong Ho Alex Yu. What is dimension?. A graphical representation of a variable by a vector or a line (usually, but not always).. One variable .  One dimension. Two variables  Two dimensions. When there are too many, the problem is called .