1) Euler's formula states that for any planar graph, the number of vertices minus the number of edges plus the number of faces is equal to 2.
2) This formula can be used to show that any planar graph must have at least one vertex of degree 5 or less.
3) The Heawood conjecture, later proven, provides a formula for determining the maximum number of colors needed to color a map on a closed surface based on the surface's Euler characteristic.
Planar graph( Algorithm and Application )Abdullah Moin
A graph is said to be planar if it can be drawn in a plane so that no edge cross. Example: The graph shown in fig is a planar graph. Region of a Graph: Consider a planar graph G=(V, E). A region is defined to be an area of the plane that is bounded by edges and cannot be further subdivided.
Title
Planar Graph Application and Algorithms.
Abstract
A Planar graph is two dimension graph. By using Planar graph visually representing network, software, design of chip and many more applications. In this report there will be discussion about non planar graph, how to convert non planar graph to planar graph and explain related theorems(Kuratowski’s theorem, Euler’s theorem).This report also contain an example of The House-and-utility problem which part of corollaries and discussion about Petersen graph. There will be a discussion about planarity test and determine a graph is planar or not. The aim of this report represent the basic knowledge of planar graph and provide a view on this particular topic.
Attributed Graph Matching of Planar GraphsRaül Arlàndez
Many fields such as computer vision, scene analysis, chemistry and molecular biology have
applications in which images have to be processed and some regions have to be searched for
and identified. When this processing is to be performed by a computer automatically without
the assistance of a human expert, a useful way of representing the knowledge is by using
attributed graphs. Attributed graphs have been proved as an effective way of representing
objects. When using graphs to represent objects or images, vertices usually represent regions
(or features) of the object or images, and edges between them represent the relations
between regions. Nonetheless planar graphs are graphs which can be drawn in the plane
without intersecting any edge between them. Most applications use planar graphs to
represent an image.
Graph matching (with attributes or not) represents an NP-complete problem, nevertheless
when we use planar graphs without attributes we can solve this problem in polynomial time
[1]. No algorithms have been presented that solve the attributed graph-matching problem and
use the planar-graphs properties. In this master thesis, we research about Attributed-Planar-
Graph matching. The aim is to find a fast algorithm through studying in depth the properties
and restrictions imposed by planar graphs.
Planar graph( Algorithm and Application )Abdullah Moin
A graph is said to be planar if it can be drawn in a plane so that no edge cross. Example: The graph shown in fig is a planar graph. Region of a Graph: Consider a planar graph G=(V, E). A region is defined to be an area of the plane that is bounded by edges and cannot be further subdivided.
Title
Planar Graph Application and Algorithms.
Abstract
A Planar graph is two dimension graph. By using Planar graph visually representing network, software, design of chip and many more applications. In this report there will be discussion about non planar graph, how to convert non planar graph to planar graph and explain related theorems(Kuratowski’s theorem, Euler’s theorem).This report also contain an example of The House-and-utility problem which part of corollaries and discussion about Petersen graph. There will be a discussion about planarity test and determine a graph is planar or not. The aim of this report represent the basic knowledge of planar graph and provide a view on this particular topic.
Attributed Graph Matching of Planar GraphsRaül Arlàndez
Many fields such as computer vision, scene analysis, chemistry and molecular biology have
applications in which images have to be processed and some regions have to be searched for
and identified. When this processing is to be performed by a computer automatically without
the assistance of a human expert, a useful way of representing the knowledge is by using
attributed graphs. Attributed graphs have been proved as an effective way of representing
objects. When using graphs to represent objects or images, vertices usually represent regions
(or features) of the object or images, and edges between them represent the relations
between regions. Nonetheless planar graphs are graphs which can be drawn in the plane
without intersecting any edge between them. Most applications use planar graphs to
represent an image.
Graph matching (with attributes or not) represents an NP-complete problem, nevertheless
when we use planar graphs without attributes we can solve this problem in polynomial time
[1]. No algorithms have been presented that solve the attributed graph-matching problem and
use the planar-graphs properties. In this master thesis, we research about Attributed-Planar-
Graph matching. The aim is to find a fast algorithm through studying in depth the properties
and restrictions imposed by planar graphs.
The questions have been designed to test for deep understanding of math concepts. Detailed explanations and solutions of these questions are also provided.
The questions have been designed to test for deep understanding of math concepts. Detailed explanations and solutions of these questions are also provided.
1. Euler’s formula and colou rings of gra
Euler's formula states v − e + f = 2. This together with the fact that each edge is shared by
two regions, 2e = 3f, can be used to show 6v − 2e = 12. Now, the degree of a vertex is the
number of edges abutting it. If vn is the number of vertices of degree n and D is the
maximum degree of any vertex,
But since 12 > 0 and 6 − i ≤ 0 for all i ≥ 6, this demonstrates that there is at least one
vertex of degree 5 or less.
The intuitive idea underlying discharging is to consider the planar graph as an electrical
network. Initially positive and negative "electrical charge" is distributed amongst the
vertices so that the total is positive.
Recall the formula above:
One can also consider the coloring problem on surfaces other than the plane (Weisstein).
The problem on the sphere or cylinder is equivalent to that on the plane. For closed
(orientable or non-orientable) surfaces with positive genus, the maximum number p of
colors needed depends on the surface's Euler characteristic χ according to the formula
where the outermost brackets denote the floor function.
(Weisstein).
This formula, the Heawood conjecture, was conjectured by P.J. Heawood in 1890 and
proven by Gerhard Ringel and J. T. W. Youngs in 1968. The only exception to the
formula is the Klein bottle, which has Euler characteristic 0 (hence the formula gives p =
7) and requires 6 colors, as shown by P. Franklin in 1934 (Weisstein).
2. For example, the torus has Euler characteristic χ = 0 (and genus g = 1) and thus p = 7, so
no more than 7 colors are required to color any map on a torus
V-E+F=15-20+7=2
Euler's formula states that for a map on the sphere, , where is the
number of vertices, is the number of faces, and is the number of edges.
This Demonstration shows a map in the plane (so the exterior face counts as
a face). The formula is proved by deleting edges lying in a cycle (which
causes and to each decrease by one) until there are no cycles left. Then
one has a tree, and one can delete vertices of degree one and the edges
connected to them until only a point is left. Each such move decreases and
by one. So all the moves leave unchanged, but at the end and are
each 1 and is 0, so must have been 2 at the start.
Euler’s formula and planar graphs