# Graph Theory,Graph Terminologies,Planar Graph & Graph Colouring

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### Graph Theory,Graph Terminologies,Planar Graph & Graph Colouring

• 1. GRAPH THEORY RAJAT(3026) RAJESH() SAURABH (3174) SHAFAQ (3122)
• 2. CONTENTS • 8.1 GRAPH & GRAPH MODELS • 8.2 GRAPH TERMINOLOGIES • 8.7 PLANAR GRAPH • 8.8 GRAPH COLOURING
• 3. Introduction What is a graph ? • It is a pair G = (V, E), where  V = V(G) = set of vertices  E = E(G) = set of edges • Example:  V = {s, u, v, w, x, y, z}  E = {(x,s), (x,v), (x,u), (v,w), (s,v), (s,u), (s,w), (s,y), (w,y), (u,y), (u,z),(y,z)}
• 4. Edges • An edge may be labeled by a pair of vertices, for instance e = (v,w). • e is said to be incident on v and w. • Isolated vertex = a vertex without incident edges.
• 5. Simple graph A simple graph G (V , E) consists of a non-empty set of vertices ‘V’ and a set ‘E’ of edges, such that each edge ‘e’ belongs to E is associated with an unordered pair of distinct vertices, called its endpoints. It is a simple graph. loop Multiple edges It is not simple graph.
• 6. Multigraphs (or Pseudo-graphs) • Loop : An edge whose endpoints are same • Multiple edges : Edges have the same pair of endpoints Loop Multiple edges Unlike simple graphs, it contains the edges with same endpoints. Note: Multigraphs doesn’t contain loops.
• 7. Digraphs A digraph is a pair G = (V , A) of a set ‘V’, whose elements are called vertices or nodes, With a set ‘A’ of ordered pairs of vertices, called arcs, directed edges, or arrows (and sometimes simply edges with the corresponding set named E instead of A). multiple arc arc loop node
• 8. Graph Terminology Type Edges Multiple Edges Allowed? Loops Allowed? Simple graph Undirected No No Multigraph Undirected Yes No Pseudo graph Undirected Yes Yes Simple Directed graph Directed No No Directed Multigraph Directed Yes Yes Mixed graph Undirected & Directed Yes Yes
• 9. Graph Models 1. NICHE OVERLAP Graphs in Ecosystem Graphs are used in many models involving the interaction of different species of animals. For instance, the competition b/w species in an ecosystem can be modeled using a niche overlap graph.
• 10. 2. Round-Robin Tournaments A tournament where each team plays each other team exactly once is called a round-robin tournament. Such tournaments can be modeled using directed graphs where each team is represented by a vertex. Note: (a , b) is an edge if team a beats team b and if the edge is directed in the vertex then that team is defeated by the other one and vice-versa.
• 11. Degree of graph The degree of a vertex in a simple graph, denoted by deg(v), is the number of edges incident on it. Degree also means number of adjacent vertices. For a Node, • The number of head endpoints adjacent to a node is called the Indegree and it is denoted by deg- (v). • The number of tail endpoints adjacent to a node is called Outdegree and it is denoted by deg+ (v).
• 12. For example in the digraph, Out-deg(1) = 2 In-deg(1) = 0 Out-deg(2) = 2 In-deg(2) = 2 Out-deg(3) = 1 In-deg(3) = 4 And so on…….
• 13. Handshaking Lemma Let G = (V , E) be an undirected graph with e edges. Then, 2e = 풗∈푽 풅풆품( ). For example, How many edges are there in a graph with 10 vertices each of degree six? Solution: deg( ) = 6*10 = 60 which follows 2e = 60 ,i.e., e = 30
• 14. Corollary of Handshaking theorem Theorem: In any simple graph, there are an even number of vertices of odd degree. Proof : Let V1 and V2be the set of vertices of even degree and the set of vertices of odd degree, respectively, in an undirected graph G = (V , E). Then 2e= 푣∈푉 deg = 푣∈푉1 deg + 푣∈푉2 deg Because deg( ) is even for v ∈ V1. Since the L.H.S. that is 2e is even thus the second term in last inequality ,which is 푣∈푉2 deg , must be even. Because all the terms in this sum are odd, there must be even number of such terms. Thus, there are an even number of vertices of odd degree.
• 15. Some Special Graphs 1. Complete graph Kn : The complete graph Kn is the graph with ‘n’ vertices and every pair of vertices is joined by an edge , like in Mesh topology. The figure represents K2,K3,………,K7.
• 16. 2. Cycle Graph : A cycle graph Cn , where n >= 3, sometimes simply known as an n- Cycle, is a graph on n nodes , 1,2,…….,n , and edges {1,2} , {2,3} , …. , {n-1,n} , {n,1}. C5 CCC3 4 6
• 17. 3. Wheel Graph : A Wheel graph Wn contain an additional vertex to the cycle Cn, for n>=3 , and connect this new vertex to each of the n vertices in Cn, by new edges. The wheels W3 , W4 , W5 , W6 are displayed below. W5 WWW3 4 6
• 18. 4. N-Cube : The n-cube (hypercube) Qn is the graph whose vertices represent 2n bit strings of length n. Two vertices are adjacent if and only if the bit strings differ in exactly one position. 100 000 110 111 101 010 011 001 Figure represents Q3
• 19. Bipartite Graphs • A bipartite graph G is a graph such that – V(G) = V(G1)  V(G2) – |V(G1)| = m, |V(G2)| = n – V(G1) V(G2) = Ø No edges exist between any two vertices in the same subset V(Gk), k = 1,2 G1 G2
• 20. Complete Bipartite graph Km,n A bipartite graph is the complete bipartite graph Km,n if every vertex in V(G1) is joined to a vertex in V(G2) and conversely, |V(G1)| = m , |V(G2)| = n
• 21. Bipartite Graphs in terms of Graph Coloring Theorem: A simple graph is bipartite if and only if it is possible to assign one of two different colors to each vertex of the graph so that no two adjacent vertices are assigned the same color. K2 , 3 V V2 1
• 22. Proof: First, suppose that G = (V , E) is a simple graph. Then V = V1 U V2, where V1 and V2 are disjoint sets and every edge in E connects a vertex in V1 and a vertex in V2. If we assign one color to each vertex in V1 and a second color to each vertex in V2, then no two adjacent vertices are assigned the same color. Now suppose that it is possible to assign colors to the vertices of the graph using just two colors so that no two adjacent vertices are assigned the same color. Let V1 be the set of vertices assigned one color and V2 be the set of vertices assigned the other color. Then, V1 and V2 are disjoint and V = V1 U V2. Furthermore, every edge connects a vertex in V1 and a vertex in V2 because no two adjacent vertices are either both in V1 or both in V2. Consequently, G is bipartite.
• 23. Applications of Special Types of Graphs 1. Job Assignments : Suppose that there are m employees in a group and j different jobs that need to be done where m<=j. Each employee is trained to do one or more of these j jobs. We can use a graph to model employee capabilities. We represent each employee by a vertex and each job by a vertex. For each employee, we include an edge from the vertex representing that employee to the vertices representing all jobs the employee has been trained to do.
• 24. 2. Local Area Network : Various computers in a building, such as minicomputers and personal computers, as well as peripheral devices such as printers and plotters, can be where all devices are connected to a central control device. A local network can be represented using a complete bipartite graph K1,n. Messages are sent from device to device through the central control device. Other local area networks are based on a ring topology, where each device is connected to exactly two others. Local area networks with a ring topology are modeled using n-cycles, Cn. Messages are sent from device to device around the cycle until the intended recipient of a message is reached.
• 25. PLANAR GRAPHS : A graph is called planar if it can be drawn in the plane without any edges crossing , (where a crossing of edges is the intersection of lines or arcs representing them at a point other than their common endpoint). Such a drawing is called a planar representation of the graph.
• 26. Showing K4 is planar. Showing Q3 is non-planar.
• 27. Consider the problem of joining three houses to each of three separate utilities as shown in the figure. Is it possible to join these houses and utilities so that none of the connections cross? This problem can be modeled using the complete bipartite graph K3,3 .
• 28. SOLUTION: Any attempt to draw K3,3 in the plane with no edges crossing is doomed. We now show why. In any planar representation of K3,3 , the vertices 1 and 2 must be connected to both 4 and 5. These four edges form a closed curve that splits the plane into two regions, R1 and R2 , as shown in figure on L.H.S. 1 5 R2 1 5 R21 R1 R1 3 R22 4 2 4 2 Now, the vertex 3 is in either R1 or R2. When 3 is in R2, the inside of the closed curve, the edges b/w 3 and 4 & b/w 3 and 5 separate R2 into two subregions, R21 and R22, as shown in fig on R.H.S.
• 29. Next, note that there is no way to place the final vertex 6 without forcing a crossing. • For if 6 is in R1, then the edge b/w 6 and 3 cannot be drawn without a crossing. • If 6 is in R21, then the edge b/w 2 and 6 cannot be drawn without a crossing. If 6 is in R22, then the edge b/w 1 and 6 cannot be drawn without a crossing.
• 30. EULER’S FORMULA Since, a planar representation of a graph splits the plane into regions, including an unbounded region. For e.g., R2 R1 R5 R3 R4 R6 Acc. To Euler’s formula, If G be a connected planar simple graph with e edges and v vertices. Let r be the number of regions in a planar representation of G. Then r = e – v + 2 and this no. will remain same in all planar representations of that graph.
• 31. Proof: First, we specify a planar representation of G. We will prove the theorem by constructing a seq of sub-graphs G1,G2,……..,Ge = G, successively adding an edge at each stage. This is done using the following inductive definition. • Base Case: If e = 1, the graph consists of two vertex (i.e., v = 2) with a single region surrounding it. So we have r = 1 – 2 + 2 which is clearly right. R1 1 2
• 32. Inductive step: Now assume that rn = en – vn + 2. Let {an+1, bn+1} be the edge that is added to Gn to obtain Gn+1. There are two possibilities: 1) Both an+1 and bn+1 are already in Gn. These two vertices must be on the boundary of a common region R, or else it would be impossible to add the edge {an+1, bn+1} to Gn w/o two edges crossing (and Gn+1 is planar). In this case rn+1 = rn + 1, en+1 = en + 1 and vn+1 = vn. Thus, each side of the formula relating the number of regions, edges and vertices increases by exactly one, i.e., rn+1 = en+1 – vn+1 + 2
• 33. 2) One of the two vertices of the new edge is not already in Gn. Suppose that an+1 is in Gn but that bn+1 is not. Adding this new edge does not produce any new regions, because bn+1 must be in a region that has an+1 on its boundary. Consequently, rn+1 = rn. Moreover, en+1 = en + 1 and vn+1 = vn + 1. Each side of the formula relating the number of regions, edges and vertices remains the same, so the formula is still true, i.e., rn+1 = en+1 – vn+1 + 2
• 34. Corollary of Euler’s Formula C-1 : If G is a connected planar simple graph with e edges and v vertices, where v>=3, then e <=3v-6. Proof: Let a connected planar simple graph divides the plane into r regions. Then the degree of each region is at least 3 because the graphs we discussed here are simple graphs. In particular, note that the degree of the unbounded region is at least 3 because there are at least three vertices in the graph.
• 35. Note that the sum of the degrees of the regions is exactly twice the no. of edges in the graph, because each edge occurs on the boundary of a region exactly twice. Because each region has degree greater than or equal to 3, it follows 2e = Σ deg(R) >= 3r Hence, (2/3)e >= r Using r = e – v + 2 (Euler’s theorem) e – v + 2 <= (2/3) e i.e., e/3 <= v - 2 which gives e <= 3v – 6. Use this corollary to demonstrate that K5 is not planar.
• 36. But if we implies corollary 1 to prove for K3,3 is planar then it follows the corollary but when we try to draw K3,3 then we found that it is planar! Corollary 2 : If a connected planar simple graph has e edges and v vertices with v>=3 and no circuits of length 3, then e <= 2v – 4 for K3,3 e = 9 and 2v – 4 = 8 , shows that K3,3 is non-planar.
• 37. Kuratowski’s Theorem We’ve seen that K3,3 and K5 are not planar. Clearly, a graph is not planar if it contains either of these two graphs as a sub-graph. If a graph is planar, so will be any graph obtained by removing an edge {u, v} and adding a new vertex w together with edges {u, w} and {w, v}. Such an operation is called an elementary subdivision. The graphs G1 = ( V1, E1) and G2 = (V2, E2) are called homeomorphic if they obtained from the same graph by a sequence of elementary subdivisions.
• 38. Theorem 2: A graph is non-planar if and only if it contains a subgraph homeomorphic to K3,3 or K5. G has a subgraph homeomorphic to K5. H is obtained by deleting h, j, and k and all edges incident with these vertices. H is homeomorphic to K5 because it can be obrained from K5 (with vertices a, b, c, g and i) by a sequence of elementary subdivisions, adding the vertices d, e and f. Hence, G is non-planar.
• 39. Graph Coloring : A coloring of a simple graph is the assignment of a colour to each vertex of the graph so that no two adjacent vertices are assigned the same colour. • First, make the dual graph of a map which u want to colour. • Then, a graph can be coloured by assigning a different colour to each of its vertices. However, one can find fewer no. of colours than the vertices in the graph to colour it.
• 40. For example, B A E C D Now, we can see from the map that atleast four colours are required for colouring . By forming the dual graph of the map we can now assign colour to each vertex of the graph.
• 41. Chromatic Number: The chromatic no. of a graph is the least no. of colours needed for a coloring of this graph. The chromatic no. of a graph G is denoted by 휒 퐺 . For example , Chromatic no. of Kn is n. A coloring of K5 using five colours is given by,
• 42. Greedy Coloring (Algorithm) • χ(G) ≤ Δ(G) + 1 • Greedy Algorithm: – Put the vertices of a graph in a sequence – For each vertex in the sequence, assign it the lowest indexed color not already assigned to adjacent vertices • Not guaranteed to be optimal for every possible sequence • Guaranteed optimal for at least one sequence
• 43. Greedy Coloring Example Vertex 0 1 2 3 4 5 Color Yellow Yellow Yellow Green Green Green
• 44. Greedy Coloring Example Vertex 0 3 1 4 2 5 Color Yellow Yellow Green Green Purple Purple
• 45. Chromatic polynomial • Polynomial which gives the number of ways of proper coloring a graph using a given number of colors • Ci = no. of ways to properly color a graph using exactly i colors • λ = total no of colors • λ Ci = selecting I colors out of λ colors • ΣCi λ Ci = total number of ways a graph canbe properly colored using λ or lesser no. of colors Pn(λ) of G = ΣCi λ Ci
• 46. K-chromatic Graph Let G be a simple graph, and let PG(k) be the number of ways of coloring the vertices of G with k colors in such a way that no two adjacent vertices are assigned the same color. The function PG(k) is called the chromatic polynomial of G. As an example, consider complete graph K3 as shown in the following figure.
• 47. • Then the top vertex can be assigned any of the k colors, the left vertex can be assigned any k-1 colors, and right vertex can be assigned any of the k-2 colors. • The chromatic polynomial of K3 is therefore K(K -1)(K -2). The extension of this immediately gives us the following result. • If G is the complete graph Kn, then Pn(K) = K(K - 1)(K - 2) . . . (K - n +1).
• 48. Applications: Sudoku • Each cell is a vertex • Each integer label is a “color” • A vertex is adjacent to another vertex if one of the following hold: – Same row – Same column – Same 3x3 grid • Vertex-coloring solves Sudoku
• 49. Applications: Scheduling problem Given is a list of courses, and for each course the list of students who want to register for it. The goal is to find a time slot for each course (using as few slots as possible). If a student is registered for courses p and q, add an edge between p and q. If the resulting class can be colored with k = the number of available slots (MWF 8 -9 , MWF 9 – 10 etc. are various slots), then we can find a schedule that will allow the students to take all the courses they want to.
• 50. Applications: Traffic signal design Traffic signal design: At an intersection of roads, we want to install traffic signal lights which will periodically switch between green and red. The goal is to reduce the waiting time for cars before they get green signal. This problem can be modeled as a coloring problem. Each path that crosses the intersection is a node. If two paths intersect each other, there is an edge connecting them. Each color represents a time slot at which the path gets a green light.
• 51. Applications • Scheduling • Register allocation • VLSI channel routing • Biological networks (Khor) • Testing printed circuit boards (Garey, Johnson, & Hing)
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