Chapter 1 Fundamental Concept Graph Theory Chapter 1 Fundamental Concept 1.1 What Is a Graph? 1.2 Paths, Cycles, and Trails 1.3 Vertex Degree and Counting 1.4 Directed Graphs Ch. 1. Fundamental Concept

The KÖnigsberg Bridge Problem Graph Theory The KÖnigsberg Bridge Problem Königsber is a city on the Pregel river in Prussia The city occupied two islands plus areas on both banks Problem: Whether they could leave home, cross every bridge exactly once, and return home. X Y Z W Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory A Model A vertex : a region An edge : a path(bridge) between two regions e1 e2 e3 e4 e6 e5 e7 Z Y X W X Y Z W Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory General Model A vertex : an object An edge : a relation between two objects common member Committee 1 Committee 2 Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory What Is a Graph? A graph G is a triple consisting of: A vertex set V(G ) An edge set E(G ) A relation between an edge and a pair of vertices e1 e2 e3 e4 e6 e5 e7 Z Y X W Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Loop, Multiple edges Loop : An edge whose endpoints are equal Multiple edges : Edges have the same pair of endpoints Multiple edges loop Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Simple Graph Simple graph : A graph has no loops or multiple edges Multiple edges loop It is not simple. It is a simple graph. Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Adjacent, neighbors Two vertices are adjacent and are neighbors if they are the endpoints of an edge Example: A and B are adjacent A and D are not adjacent A B C D Ch. 1. Fundamental Concept

Finite Graph, Null Graph Graph Theory Finite Graph, Null Graph Finite graph : an graph whose vertex set and edge set are finite Null graph : the graph whose vertex set and edges are empty Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Complement Complement of G: The complement G’ of a simple graph G : A simple graph V(G’) = V(G) E(G’) = { uv | uv E(G) } G’ u v w x y u v w x y G Ch. 1. Fundamental Concept

Clique and Independent set Graph Theory Clique and Independent set A Clique in a graph: a set of pairwise adjacent vertices (a complete subgraph) An independent set in a graph: a set of pairwise nonadjacent vertices Example: {x, y, u} is a clique in G {u, w} is an independent set u G y v w x Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Bipartite Graphs A graph G is bipartite if V(G) is the union of two disjoint independent sets called partite sets of G Also: The vertices can be partitioned into two sets such that each set is independent Matching Problem Job Assignment Problem Workers Boys Girls Jobs Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Chromatic Number The chromatic number of a graph G, written x(G), is the minimum number of colors needed to label the vertices so that adjacent vertices receive different colors Red Green Blue x(G) = 3 Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Maps and coloring A map is a partition of the plane into connected regions Can we color the regions of every map using at most four colors so that neighboring regions have different colors? Map Coloring  graph coloring A region  A vertex Adjacency  An edge Ch. 1. Fundamental Concept

Scheduling and graph Coloring 1 Graph Theory Scheduling and graph Coloring 1 Two committees can not hold meetings at the same time if two committees have common member Committee 1 Committee 2 common member Ch. 1. Fundamental Concept

Scheduling and graph Coloring 1 Graph Theory Scheduling and graph Coloring 1 Model: One committee being represented by a vertex An edge between two vertices if two corresponding committees have common member Two adjacent vertices can not receive the same color common member Committee 1 Committee 2 Ch. 1. Fundamental Concept

Scheduling and graph Coloring 2 Graph Theory Scheduling and graph Coloring 2 Scheduling problem is equivalent to graph coloring problem Committee 2 Common Member Different Color Common Member Committee 1 Committee 3 No Common Member Same Color OK Same time slot OK Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Path and Cycle Path : a sequence of distinct vertices such that two consecutive vertices are adjacent Example: (a, d, c, b, e) is a path (a, b, e, d, c, b, e, d) is not a path; it is a walk Cycle : a closed Path Example: (a, d, c, b, e, a) is a cycle a b c e d Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Subgraphs A subgraph of a graph G is a graph H such that: V(H)  V(G) and E(H)  E(G) and The assignment of endpoints to edges in H is the same as in G. Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Subgraphs Example: H1, H2, and H3 are subgraphs of G a b G c d e b a a b H3 c d c H1 H2 e d d e Ch. 1. Fundamental Concept

Connected and Disconnected Graph Theory Connected and Disconnected Connected : There exists at least one path between two vertices Disconnected : Otherwise Example: H1 and H2 are connected H3 is disconnected a b d e a b c d c d H3 H1 H2 e Ch. 1. Fundamental Concept

Adjacency, Incidence, and Degree Graph Theory Adjacency, Incidence, and Degree Assume ei is an edge whose endpoints are (vj,vk) The vertices vj and vk are said to be adjacent The edge ei is said to be incident upon vj Degree of a vertex vk is the number of edges incident upon vk . It is denoted as d(vk) ei vj vk Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Adjacency matrix Let G = (V, E), |V| = n and |E|=m The adjacency matrix of G written A(G), is the n-by-n matrix in which entry ai,j is the number of edges in G with endpoints {vi, vj}. w x y z 0 1 1 0 1 0 2 0 1 2 0 1 0 0 1 0 wxyz a b c d e w x y z Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Incidence Matrix Let G = (V, E), |V| = n and |E|=m The incidence matrix M(G) is the n-by-m matrix in which entry mi,j is 1 if vi is an endpoint of ei and otherwise is 0. w a b c d e 1 1 0 0 0 1 0 1 1 0 0 1 1 1 1 0 0 0 0 1 b wxyz y a c z e d x Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Isomorphism An isomorphism from a simple graph G to a simple graph H is a bijection f:V(G)V(H) such that uv E(G) if and only if f(u)f(v)  E(H) We say “G is isomorphic to H”, written G  H f1: w x y z c b d a w y c d H G z a x b f2: w x y z a d b c Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Complete Graph Complete Graph : a simple graph whose vertices are pairwise adjacent Complete Graph Ch. 1. Fundamental Concept

Complete Bipartite Graph or Biclique Graph Theory Complete Bipartite Graph or Biclique Complete bipartite graph (biclique) is a simple bipartite graph such that two vertices are adjacent if and only if they are in different partite sets. Complete Bipartite Graph Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Petersen Graph 1.1.36 The petersen graph is the simple graph whose vertices are the 2-element subsets of a 5-element set and whose edges are pairs of disjoint 2-element subsets Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Petersen Graph 1.1.37 Assume: the set of 5-element be (1, 2, 3, 4, 5) Then, 2-element subsets: (1,2) (1,3) (1,4) (1,5) (2,3) (2,4) (2,5) (3,4) (3,5) (4,5) 12 34 15 23 45 35 13 14 24 25 45: (4, 5) Disjoint, so connected Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Petersen Graph 1.1.36 Three drawings Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Theorem: If two vertices are non-adjacent in the Petersen Graph, then they have exactly one common neighbor. 1.1.38 Proof: No connection, Joint, One common element. 3 elements in these vertices totally x, y x, z u, v Since 5 elements totally, 5-3 elements left. Hence, exactly one of this kind. Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Girth 1.1.39, 1.1.40 Girth : the length of its shortest cycle. If no cycles, girth is infinite Ch. 1. Fundamental Concept

Girth and Petersen graph 1.1.39, 1.1.40 Graph Theory Girth and Petersen graph 1.1.39, 1.1.40 Theorem: The Petersen Graph has girth 5. Proof: Simple  no loop  no 1-cycle (cycle of length 1) Simple  no multiple  no 2-cycle 5 elements no three pair-disjoint 2-sets  no 3-cycle By previous theorem, two nonadjacent vertices has exactly one common neighbor  no 4-cycle 12-34-51-23-45-12 is a 5-cycle. Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Walks, Trails1.2.2 A walk : a list of vertices and edges v0, e1, v1, …., ek, vk such that, for 1  i  k, the edge ei has endpoints vi-1 and vi . A trail : a walk with no repeated edge. Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Paths 1.2.2 A u,v-walk or u,v-trail has first vertex u and last vertex v; these are its endpoints. A u,v-path: a u,v-trail with no repeated vertex. The length of a walk, trail, path, or cycle is its number of edges. A walk or trail is closed if its endpoints are the same. Ch. 1. Fundamental Concept

Lemma: Every u,v-walk contains a u,v-path 1.2.5 Graph Theory Lemma: Every u,v-walk contains a u,v-path 1.2.5 Proof: Use induction on the length of a u, v-walk W. Basis step: l = 0. Having no edge, W consists of a single vertex (u=v). This vertex is a u,v-path of length 0. to be continued Ch. 1. Fundamental Concept

Lemma: Every u,v-walk contains a u,v-path 1.2.5 Graph Theory Lemma: Every u,v-walk contains a u,v-path 1.2.5 Proof: Continue Induction step : l  1. Suppose that the claim holds for walks of length less than l. If W has no repeated vertex, then its vertices and edges form a u,v-path. Ch. 1. Fundamental Concept

Lemma: Every u,v-walk contains a u,v-path 1.2.5 Graph Theory Lemma: Every u,v-walk contains a u,v-path 1.2.5 Proof: Continue Induction step : l  1. Continue If W has a repeated vertex w, then deleting the edges and vertices between appearances of w (leaving one copy of w) yields a shorter u,v-walk W’ contained in W. By the induction hypothesis, W ’ contains a u,v-path P,and this path P is contained in W. Delete Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Components 1.2.8 The components of a graph G are its maximal connected subgraphs A component (or graph) is trivial if it has no edges; otherwise it is nontrivial An isolated vertex is a vertex of degree 0 Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Theorem: Every graph with n vertices and k edges has at least n-k components 1.2.11 Proof: An n-vertex graph with no edges has n components Each edge added reduces this by at most 1 If k edges are added, then the number of components is at least n - k Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Theorem: Every graph with n vertices and k edges has at least n-k components 1.2.11 Examples: n =2, k =1, 1 component n =6, k =3, 4 components n =3, k =2, 1 component n =6, k =3, 3 components Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Cut-edge, Cut-vertex 1.2.12 A cut-edge or cut-vertex of a graph is an edge or vertex whose deletion increases the number of components Not a Cut-vertex Cut-edge Cut-vertex Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Cut-edge, Cut-vertex 1.2.12 G-e or G-M : The subgraph obtained by deleting an edge e or set of edges M G-v or G-S : The subgraph obtained by deleting a vertex v or set of vertices S G G-e e Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Induced subgraph 1.2.12 An induced subgraph : A subgraph obtained by deleting a set of vertices We write G[T] for G-T’, where T’ =V(G)-T G[T] is the subgraph of G induced by T Example: Assume T:{A, B, C, D} B B A A E C D C D G[T] G Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Induced subgraph 1.2.12 More Examples: G2 is the subgraph of G1 induced by (A, B, C, D) G3 is the subgraph of G1 induced by (B, C) G4 is not the subgraph induced by (A, B, C, D) B B B A B A A C E C D C D C D G3 G1 G2 G4 Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Induced subgraph 1.2.12 A set S of vertices is an independent set if and only if the subgraph induced by it has no edges. G3 is an example. B B A C E C D G3 G1 Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Theorem: An edge e is a cut-edge if and only if e belongs to no cycles. 1.2.14 Proof :1/2 Let e= (x, y) be an edge in a graph G and H be the component containing e. Since deletion of e effects no other component, it suffices to prove that H-e is connected if and only if e belongs to a cycle. First suppose that H-e is connected. This implies that H-e contains an x, y-path, This path completes a cycle with e. Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Theorem: An edge e is a cut-edge if and only if e belongs to no cycles. 1.2.14 Proof :2/2 Now suppose that e lies in a cycle C. Choose u, vV(H) Since H is connected, H has a u, v-path P If P does not contain e Then P exists in H-e Otherwise (P contains e) Suppose by symmetry that x is between u and y on P Since H-e contains a u, x-path along P, the transitivity of the connection relation implies that H-e has a u, v-path We did this for all u, v  V(H), so H-e is connected. Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Theorem: An edge e is a cut-edge if and only if e belongs to no cycles. 1.2.14 An Example: Ch. 1. Fundamental Concept

Lemma: Every closed odd walk contains an odd cycle Graph Theory Lemma: Every closed odd walk contains an odd cycle Proof:1/3 Use induction on the length l of a closed odd walk W. l=1. A closed walk of length 1 traverses a cycle of length 1. We need to prove the claim holds if it holds for closed odd walks shorter than W. Ch. 1. Fundamental Concept

Lemma: Every closed odd walk contains an odd cycle Graph Theory Lemma: Every closed odd walk contains an odd cycle Proof: 2/3 Suppose that the claim holds for closed odd walks shorter than W. If W has no repeated vertex (other than first = last), then W itself forms a cycle of odd length. Otherwise, (W has repeated vertex ) Need to prove: If repeated, W includes a shorter closed odd walk. By induction, the theorem hold Ch. 1. Fundamental Concept

Lemma: Every closed odd walk contains an odd cycle Graph Theory Lemma: Every closed odd walk contains an odd cycle Proof: 3/3 If W has a repeated vertex v, then we view W as starting at v and break W into two v,v-walks Since W has odd length, one of these is odd and the other is even. (see the next page) The odd one is shorter than W, by induction hypothesis, it contains an odd cycle, and this cycle appears in order in W Odd = Odd + Even v Odd Even Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Theorem: A graph is bipartite if and only if it has no odd cycle. 1.2.18 Examples: A B A B F C D E D C A B A B C D C D E F Ch. 1. Fundamental Concept

Theorem: A graph is bipartite if it has no odd cycle. 1.2.18 Graph Theory Theorem: A graph is bipartite if it has no odd cycle. 1.2.18 Proof: (sufficiency1/3) Let G be a graph with no odd cycle. We prove that G is bipartite by constructing a bipartition of each nontrivial component H. For each v  V (H ), let f (v ) be the minimum length of a u, v -path. Since H is connected, f (v ) is defined for each v  V (H ) . Ch. 1. Fundamental Concept

Theorem: A graph is bipartite if it has no odd cycle. 1.2.18 Graph Theory Theorem: A graph is bipartite if it has no odd cycle. 1.2.18 Proof: (sufficiency2/3) Let X={v  V (H ): f (v ) is even} and Y={v  V(H ): f (v ) is odd} An edge v, v’ within X (or Y ) would create a closed odd walk using a shortest u, v-path, the edge v, v’ within X (or Y ) and the reverse of a shortest u, v’-path. A closed odd walk using a shortest u, v-path, the edge v, v’ within X (or Y) , and the reverse of a shortest u, v’-path. Ch. 1. Fundamental Concept

Theorem: A graph is bipartite if it has no odd cycle. 1.2.18 Graph Theory Theorem: A graph is bipartite if it has no odd cycle. 1.2.18 Proof: (sufficiency3/3) By Lemma 1.2.15, such a walk must contain an odd cycle, which contradicts our hypothesis Hence X and Y are independent sets. Also XY = V(H), so H is an X, Y-bipartite graph Because: even (or odd) + even (or odd) = even even + 1 = odd Since no odd cycles, vv’ doesn’t exist. We have: X and Y are independent sets Even (or Odd) Odd Cycle Even (or Odd) Ch. 1. Fundamental Concept

Theorem: A graph is bipartite only if it has no odd cycle. 1.2.18 Graph Theory Theorem: A graph is bipartite only if it has no odd cycle. 1.2.18 Proof: (necessity) Let G be a bipartite graph. Every walk alternates between the two sets of a bipartition So every return to the original partite set happens after an even number of steps Hence G has no odd cycle Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Eulerian Circuits 1.2.24 A graph is Eulerian if it has a closed trail containing all edges. We call a closed trail a circuit when we do not specify the first vertex but keep the list in cyclic order. An Eulerian circuit or Eulerian trail in a graph is a circuit or trail containing all the edges. Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Even Graph, Even Vertex1.2.24 An even graph is a graph with vertex degrees all even. A vertex is odd [even] when its degree is odd [even]. Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Maximal Path1.2.24 A maximal path in a graph G is a path P in G that is not contained in a longer path. When a graph is finite, no path can extend forever , so maximal (non-extendible) paths exist. Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Lemma: If every vertex of graph G has degree at least 2, then G contains a cycle. 1.2.25 Proof: Let P be a maximal path in G, and let u be an endpoint of P Since P cannot be extended, every neighbor of u must already be a vertex of P Since u has degree at least 2, it has a neighbor v in V (P ) via an edge not in P The edge uv completes a cycle with the portion of P from v to u Must u P v u P Impossible Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Theorem: A graph G is Eulerian if and only if it has at most one nontrivial component and its vertices all have even degree. 1.2.26 Proof: (Necessity) Suppose that G has an Eulerian circuit C Each passage of C through a vertex uses two incident edges And the first edge is paired with the last at the first vertex Hence every vertex has even degree Start (The 1st) In Out End (The last) Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Theorem: A graph G is Eulerian if and only if it has at most one nontrivial component and its vertices all have even degree. 1.2.26 Proof: (Necessity) Also, two edges can be in the same trail only when they lie in the same component, so there is at most one nontrivial component. Component 2 Component 1 If more than one components, can’t walk across the graph Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Theorem: A graph G is Eulerian if and only if it has at most one nontrivial component and its vertices all have even degree 1.2.26 Proof: (Sufficiency 1/3) Assuming that the condition holds, we obtain an Eulerian circuit using induction on the number of edges, m Basis step: m= 0. A closed trail consisting of one vertex suffices → Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Theorem: A graph G is Eulerian if and only if it has at most one nontrivial component and its vertices all have even degree. 1.2.26 Proof: (Sufficiency 2/3) Induction step: m>0. When even degrees, each vertex in the nontrivial component of G has degree at least 2. By Lemma 1.2.25, the nontrivial component has a cycle C. Let G’ be the graph obtained from G by deleting E(C). Since C has 0 or 2 edges at each vertex, each component of G’ is also an even graph. Since each component is also connected and has fewer than m edges, we can apply the induction hypothesis to conclude that each component of G’ has an Eulerian circuit. → Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Theorem: A graph G is Eulerian if and only if it has at most one nontrivial component and its vertices all have even degree. 1.2.26 Proof: (Sufficiency 3/3) Induction step: m>0. (continued) To combine these into an Eulerian circuit of G, we traverse C, but when a component of G’ is entered for the first time we detour along an Eulerian circuit of that component. This circuit ends at the vertex where we began the detour. When we complete the traversal of C, we have completed an Eulerian circuit of G. Ch. 1. Fundamental Concept

Proposition: Every even graph decomposes into cycles1.2.27 Graph Theory Proposition: Every even graph decomposes into cycles1.2.27 Proof: In the proof of Theorem 1.2.26 It is noted that every even nontrivial graph has a cycle The deletion of a cycle leaves an even graph Thus this proposition follows by induction on the number of edges Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Proposition: If G is a simple graph in which every vertex has degree at least k, then G contains a path of length at least k. If k2, then G also contains a cycle of length at least k+1. 1.2.28 Proof: (1/2) Let u be an endpoint of a maximal path P in G. Since P does not extend, every neighbor of u is in V(P). Since u has at least k neighbors and G is simple, P therefore has at least k vertices other than u and has length at least k. Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Proposition: If G is a simple graph in which every vertex has degree at least k, then G contains a path of length at least k. If k2, then G also contains a cycle of length at least k+1. 1.2.28 Proof: (2/2) If k  2, then the edge from u to its farthest neighbor v along P completes a sufficiently long cycle with the portion of P from v to u. d(u)  k v u At least k+1 vertices Length  k Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Degree1.3.1 The degree of vertex v in a graph G, written or d (v ), is the number of edges incident to v, except that each loop at v counts twice The maximal degree is (G ) The minimum degree is  (G ) G A B d(B) = 3, d(C) = 2 Δ(G) = 3, δ(G) = 2 F C E D Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Regular 1.3.1 G is regular if (G ) =  (G ) G is k-regular if the common degree is k. The neighborhood of v, written Ng (v ) or N (v ) is the set of vertices adjacent to v. 3-regular Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Order and size 1.3.2 The order of a graph G, written n (G ), is the number of vertices in G. An n-vertex graph is a graph of order n. The size of a graph G, written e (G ), is the number of edges in G. For nN, the notation [n ] indicates the set {1,…, n }. Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Proposition: (Degree-Sum Formula) If G is a graph, then vV(G)d(v) = 2e(G) 1.3.3 Proof: Summing the degrees counts each edge twice, Because each edge has two ends and contributes to the degree at each endpoint. Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Theorem: If k>0, then a k-regular bipartite graph has the same number of vertices in each partite set. 1.3.9 Proof: Let G be an X,Y - bigraph. Counting the edges according to their endpoints in X yields e (G ) = k |X |. d (x) = k x Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Theorem: If k>0, then a k-regular bipartite graph has the same number of vertices in each partite set. 1.3.9 Proof: Counting them by their endpoints in Y yields e (G )=k |Y | Thus k |X | = k |Y |, which yields |X |=|Y | when k > 0 y d (x) = k d (y) = k x Ch. 1. Fundamental Concept

A technique for counting a set 1/3 1.3.10 Graph Theory A technique for counting a set 1/3 1.3.10 Example: The Petersen graph has ten 6-cycles Let G be the Petersen graph. Being 3-regular, G has ten copies of K1,3 (claw) . We establish a one-to-one correspondence between the 6-cycles and the claws. Since G has girth 5, every 6-cycle F is an induced subgraph. see below Each vertex of F has one neighbor outside F. d(v)= 3, v V(G) If Existing, Girth =3. But Girth=5 so no such an edge Ch. 1. Fundamental Concept

A technique for counting a set 2/3 1.3.10 Graph Theory A technique for counting a set 2/3 1.3.10 Since nonadjacent vertices have exactly one common neighbor (Proposition 1.1.38), opposite vertices on F have a common neighbor outside F. Since G is 3-regular, the resulting three vertices outside F are distinct. Thus deleting V(F) leaves a subgraph with three vertices of degree 1 and one vertex of degree 3; it is a claw. Common neighbor of opposite vertices If the neighbors are not distinct, d(v)>3 Ch. 1. Fundamental Concept

A technique for counting a set 3/3 3.10 Graph Theory A technique for counting a set 3/3 3.10 It is shown that each claw H in G arises exactly once in this way. Let S be the set of vertices with degree 1 in H; S is an independent set. The central vertex of H is already a common neighbor, so the six other edges from S reach distinct vertices. Thus G-V(H) is 2-regular. Since G has girth 5, G-V(H) must be a 6-cycle. This 6-cycle yields H when its vertices are deleted. Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Proposition: The minimum number of edges in a connected graph with n vertices is n-1. 3.13 Proof: By proposition 1.2.11, every graph with n vertices and k edges has at least n-k components. Hence every n-vertex graph with fewer than n-1 edges has at least two components and is disconnected. The contrapositive of this is that every connected n-vertex graph has at least n-1 edges. This lower bound is achieved by the path Pn. Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Theorem: If G is simple n-vertex graph with (G)(n-1)/2, then G is connected. 1.3.15 Proof: 1/2 Choose u,v  V (G ). It suffices to show that u,v have a common neighbor if they are not adjacent. Since G is simple, we have |N(u) |   (G )  (n-1)/2, and similarly for v. Recall:  (G ) is the minimum degree, |N(u)| = d(u) Hence: |N(u) |   (G ) Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Theorem: If G is simple n-vertex graph with (G)(n-1)/2, then G is connected. 1.3.15 Proof: 2/2 When u and v are not connected, we have |N(u ) N(v )|  n - 2 since u and v are not in the union Using Remark A.13 of Appendix A, we thus compute Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Theorem: Every loopless graph G has a bipartite subgraph with at least e(G)/2 edges. 1.3.19 Proof: Partition V(G) into two sets X, Y. Using the edges having one endpoint in each set yields a bipartite subgraph H with bipartition X, Y. If H contains fewer than half the edges of G incident to a vertex v, then v has more edges to vertices in its own class than in the other class, as illustrated bellow. Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Theorem: Every loopless graph G has a bipartite subgraph with at least e(G)/2 edges. 1.3.19 Proof: 2/2 Moving v to the other class gains more edges of G than it loses. Using Iterative improvement approach When it terminates, we have dH(v)  dG(v)/2 for every vV(G) . Summing this and applying the degree-sum formula yields e(H)  e(G)/2. Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Example1 1.3.20 The algorithm in Theorem 1.3.19 need not produce a bipartite subgraph with the most edges, merely one with at least half the edges Local Maximum Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Example2 1.3.20 Consider the graph in the next page. It is 5-regular with 8 vertices and hence has 20 edges. The bipartition X={a,b,c,d} and Y={e,f,g,h} yields a 3-regular bipartite subgraph with 12 edges. The algorithm terminates here: switching one vertex would pick up two edges but lose three . Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Example(Cont.) 1.3.20 switching a would pick up two edges but lose three Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Example 1.3.20 Nevertheless, the bipartition X={a,b,g,h} and Y={c,d,e,f} yields a 4-regular bipartite subgraph with 16 edges. An algorithm seeking the maximal by local changes may get stuck in a local maximum. Local Maximum Global Maximum Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Theorem: The maximum number of edges in an n-vertex triangle free simple graph is  n2/4  1.3.23 Proof : 1/6 Let G be an n-vertex triangle-free simple graph. Let x be a vertex of maximum degree and d(x)=k. Since G has no triangles, there are no edges among neighbors of x. No edges between neighbors of x Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Theorem: The maximum number of edges in an n-vertex triangle free simple graph is  n2/4  1.3.23 Proof : 2/6 Hence summing the degrees of x and its nonneighbors counts at least one endpoint of every edge: vN(x)d(v)  e(G). We sum over n-k vertices, each having degree at most k, so e(G)  (n-k)k Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Theorem: The maximum number of edges in an n-vertex triangle free simple graph is  n2/4  1.3.23 At least n-k vertices Proof: 3/6 Doesn’t exist No edges exist vN(x) d(v) counts at least one endpoint of every edge At most k vertices Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Theorem: The maximum number of edges in an n-vertex triangle free simple graph is  n2/4  1.3.23 Proof: 4/6 Since (n-k)k counts the edges in Kn-k, k, we have now proved that e(G) is bounded by the size of some biclique with n vertices. i.e. e(G)  (n-k)k = |the edges in Kn-k, k | n-k k Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Theorem: The maximum number of edges in an n-vertex triangle free simple graph is  n2/4  1.3.23 Proof: 5/6 Moving a vertex of Kn-k,k from the set of size k to the set of size n-k gains k-1 edges and loses n-k edges. The net gain is 2k-1-n, which is positive for 2k>n+1 and negative for 2k<n+1. Thus e(Kn-k, k) is maximized when k is n/2  or n/2 . n-k k Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Theorem: The maximum number of edges in an n-vertex triangle free simple graph is  n2/4  1.3.23 Proof: 6/6 The product is then n2/4 for even n and (n2-1)/4 for odd n. Thus e(G)  n2/4 . The bound is best possible. It is seen that a triangle-free graph with n2/4 edges is: Kn/2,n/2 . Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Degree sequence 1.3.27 The Degree Sequence of a graph is the list of vertex degrees, usually written in non-increasing order, as d1 ….  d n . Example: x v Degree sequence: d(w), d(x), d(y), d(z), d(v) w y z Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Proposition: The nonnegative integers d1 ,…, dn are the vertex degrees of some graph if and only if di is even. 1.3.28 Proof: ½ Necessity When some graph G has these numbers as its vertex degrees, the degree-sum formula implies that di = 2e (G), which is even. Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Proposition: The nonnegative integers d1 ,…, dn are the vertex degrees of some graph if and only if di is even. 1.3.28 Proof: 2/2 Sufficiency Suppose that  di is even. We construct a graph with vertex set v1,…,vn and d(vi) = di for all i. Since  di is even, the number of odd values is even. First form an arbitrary pairing of the vertices in {vi : di is odd}. For each resulting pair, form an edge having these two vertices as its endpoints The remaining degree needed at each vertex is even and nonnegative; satisfy this for each i by placing [di /2] loops at vi Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Graphic Sequence 1.3.29 A graphic sequence is a list of nonnegative numbers that is the degree sequence of some simple graph. A simple graph “realizes” d. means: A simple graph with degree sequence d. Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Recursive condition 1.3.30 The lists (2, 2, 1, 1) and (1, 0, 1) are graphic. The graphic K2+K1 realizes 1, 0, 1. Adding a new vertex adjacent to vertices of degrees 1 and 0 yields a graph with degree sequence 2, 2, 1, 1, as shown below. Conversely, if a graph realizing 2, 2, 1, 1 has a vertex w with neighbors of degrees 2 and 1, then deleting w yields a graph with degrees 1, 0, 1. 2 2 1 K2 1 K1 1 1 Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Recursive condition 1.3.30 Similarly, to test 33333221, we seek a realization with a vertex w of degree 3 having three neighbors of degree 3. 3 3 3 3 3 2 2 1 Delete this Vertex A new degree sequence 2 2 2 3 2 2 1 Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Recursive condition 1.3.30 This exists if and only if 2223221 is graphic. (See next page) We reorder this and test 3222221. We continue deleting and reordering until we can tell whether the remaining list is realizable. If it is, then we insert vertices with the desired neighbors to walk back to a realization of the original list. The realization is not unique. The next theorem implies that this recursive test works. Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Recursive condition 1.3.30 33333221 3222221 221111 11100 2223221 111221 10111 Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Theorem. For n>1, an integer list d of size n is graphic if and only if d’ is graphic, where d’ is obtained from d by deleting its largest element  and subtracting 1 from its  next largest elements. The only 1-element graphic sequence is d1=0. 1.3.31 Proof: 1/6 For n =1, the statement is trivial. For n >1, we first prove that the condition is sufficient. Give d with d1…..dn and a simple graph G’ with degree sequence d’ For Example: We have: 1) d = 33333221 2) G’ with d’ = 2223221 We show: d is graphic G’ Ch. 1. Fundamental Concept

Graph Theory Theorem. For n>1, an integer list d of size n is graphic if and only if d’ is graphic, where d’ is obtained from d by deleting its largest element  and subtracting 1 from its  next largest elements. The only 1-element graphic sequence is d1=0. 1.3.31 Proof: 2/6 We add a new vertex adjacent to vertices in G’ with degrees d2-1,…..,d+1-1. These di are the  largest elements of d after (one copy of)  itself, Note : d2-1,…..,d+1-1 need not be the  largest numbers in d’ (see example in previous page) d : d1,d2,… dn d’ : d2-1,…..,d+1-1,… dn G’ May not be the  largest numbers New added vertex Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Theorem 1.3.31 continue To prove necessity, 3/6 Given a simple graph G realizing d , we produce a simple graph G’ realizing d’ Let w be a vertex of degree  in G, and let S be a set of  vertices in G having the “desired degrees” d2,…..,d+1 w d : d1, d2, …d, d+1,… dn d1= S:  vertices Ch. 1. Fundamental Concept

w Theorem 1.3.31 Proof: continue 4/6 Graph Theory Theorem 1.3.31 Proof: continue 4/6 If N(w)=S, then we delete w to obtain G’. w d : d1, d2, …d, d+1,… dn d1= Vertices, N(w)=S i.e. They are connected to w Delete w than we have d’ : d2-1,…..,d+1-1,… dn Ch. 1. Fundamental Concept

w Theorem 1.3.31 Proof: continue 5/6 Otherwise, Graph Theory Theorem 1.3.31 Proof: continue 5/6 Otherwise, Some vertex of S is missing from N(w). In this case, we modify G to increase |N(w)S| without changing any vertex degree. Since |N(w)S| can increase at most  times, repeating this converts G into another graph G* that realizes d and has S as the neighborhood of w. From G* we then delete w to obtain the desired graph G’ realizing d’. w d : d1, d2, …d, d+1,… dn d1= Vertices, N(w)S i.e. Some vertices are not connected to w. - We make them become connected to w without changing their degree. Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Theorem 1.3.31 Proof: continue 6/6 To find the modification when N(w)S , we choose xS and zS so that w z are connected and w x are not. We want to add wx and delete wz, but we must preserve vertex degrees. Since d(x)>d(z) and already w is a neighbor of z but not x, there must be a vertex y adjacent to x but not to z. Now we delete {wz,xy} and add {wx,yz} to increase |N(w)S| . z z  w w y y x x This y must exist. It becomes connected w Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory 2-switch 1.3.32 A 2-switch is the replacement of a pair of edges xy and zw in a simple graph by the edges yz and wx, given that yz and wx did not appear in the graph originally. Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Theorem: If G and H are two simple graphs with vertex set V, then dG(v)=dH(v) for every vV if and only if there is a sequence of 2-switches that transforms G into H. 1.3.33 Proof: Every 2-switch preserves vertex degrees, so the condition is sufficient. Conversely, when dG(v)=dH(v) for all vV , we obtain an appropriate sequence of 2-switches by induction on the number of vertices, n. If n<3, then for each d1,…..,dn there is at most one simple graph with d(vi)=di. Hence we can use n=3 as the basis step. Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Theorem. 1.3.33 (Continue) Consider n4 , and let w be a vertex of maximum degree, Let S={v1,…..,v} be a fixed set of vertices with the  highest degrees other than w As in the proof of Theorem 1.3.31, some sequence of 2-switches transforms G to a graph G* such that NG*(w)=S, and some such sequence transforms H to a graph H* such that NH*(w)=S Since NG*(w)=NH*(w), deleting w leaves simple graphs G’=G*-w and H’=H*-w with dG’(v)=dH’(v) for every vertex v Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Theorem. 1.3.33 Continue By the induction hypothesis, some sequence of 2-switches transforms G’ to H’. Since these do not involve w, and w has the same neighbors in G* and H*, applying this sequence transforms G* to H*. Hence we can transform G to H by transforming G to G*, then G* to H*, then (in reverse order) the transformation of H to H*. Ch. 1. Fundamental Concept

Directed Graph and Its edges 1.4.2 Graph Theory Directed Graph and Its edges 1.4.2 A directed graph or digraph G is a triple: A vertex set V(G), An edge set E(G), and A function assigning each edge an ordered pair of vertices. The first vertex of the ordered pair is the tail of the edge The second is the head Together, they are the endpoints. An edge is said to be from its tail to its head. The terms “head” and “tail” come from the arrows used to draw digraphs. Ch. 1. Fundamental Concept

Directed Graph and its edges 1.4.2 Graph Theory Directed Graph and its edges 1.4.2 As with graphs, we assign each vertex a point in the plane and each edge a curve joining its endpoints. When drawing a digraph, we give the curve a direction from the tail to the head. Ch. 1. Fundamental Concept

Directed Graph and its edges 1.4.2 Graph Theory Directed Graph and its edges 1.4.2 When a digraph models a relation, each ordered pair is the (head, tail) pair for at most one edge. In this setting as with simple graphs, we ignore the technicality of a function assigning endpoints to edges and simply treat an edge as an ordered pair of vertices. Ch. 1. Fundamental Concept

Loop and multiple edges in directed graph 1.4.3 Graph Theory Loop and multiple edges in directed graph 1.4.3 In a graph, a loop is an edge whose endpoints are equal. Multiple edges are edges having the same ordered pair of endpoints. A digraph is simple if each ordered pair is the head and tail of the most one edge; one loop may be present at each vertex. Multiple edges Loop Ch. 1. Fundamental Concept

Loop and multiple edges in directed graph 1.4.3 Graph Theory Loop and multiple edges in directed graph 1.4.3 In the simple digraph, we write uv for an edge with tail u and head v. If there is an edge form u to v, then v is a successor of u, and u is a predecessor of v. We write uv for “there is an edge from u to v”. Predecessor Successor Ch. 1. Fundamental Concept

Path and Cycle in Digraph 1.4.6 Graph Theory Path and Cycle in Digraph 1.4.6 A digraph is a path if it is a simple digraph whose vertices can be linearly ordered so that there is an edge with tail u and head v if and only if v immediately follows u in the vertex ordering. A cycle is defined similarly using an ordering of the vertices on the cycle. Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Underlying graph 1.4.9 The underlying graph of a digraph D: the graph G obtained by treating the edges of D as unordered pairs; the vertex set and edges set remain the same, and the endpoints of an edge are the same in G as in D, but in G they become an unordered pair. A digraph The underlying Graph Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Underlying graph 1.4.9 Most ideals and methods of graph theorem arise in the study of ordinary graphs. Digraphs can be a useful additional tool, especially in applications When comparing a digraph with a graph, we usually use G for the graph and D for the digraph. When discussing a single digraph, we often use G. Ch. 1. Fundamental Concept

Adjacency Matrix and Incidence Matrix of a Digraph 1.4.10 Graph Theory Adjacency Matrix and Incidence Matrix of a Digraph 1.4.10 In the adjacency matrix A(G) of a digraph G, the entry in position i, j is the number of edges from vi to vj. In the incidence matrix M(G) of a loopless digraph G, we set mi,j=+1 if vi is the tail of ej and mi,j= -1 if vi is the head of ej. Ch. 1. Fundamental Concept

Example of adjacency matrix 1.4.11 Graph Theory Example of adjacency matrix 1.4.11 The underlying graph of the digraph below is the graph of Example 1.1.19; note the similarities and differences in their matrices. Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Connected Digraph 1.4.12 To define connected digraphs, two options come to mind. We could require only that the underlying graph be connected. However, this does not capture the most useful sense of connection for digraphs. Ch. 1. Fundamental Concept

Weakly and strongly connected digraphs 1.4.12 Graph Theory Weakly and strongly connected digraphs 1.4.12 A graph is weakly connected if its underlying graph is connected. A digraph is strongly connected or strong if for each ordered pair u,v of vertices, there is a path from u to v. Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Eulerian Digraph 1.4.22 An Eulerian trail in digraph (or graph) is a trail containing all edges. An Eulerian circuit is a closed trail containing all edges. A digraph is Eulerian if it has an Eulerian circuit. Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Lemma. If G is a digraph with +(G)1, then G contains a cycle. The same conclusion holds when -(G) 1. 1.4.23 Proof. Let P be a maximal path in G, and u be the last vertex of P. Since P cannot be extended, every successor of u must already be a vertex of P. Since +(G)1, u has a successor v on P. The edge uv completes a cycle with the portion of P from v to u. Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Theorem: A digraph is Eulerian if and only if d+(v)=d-(v) for each vertex v and the underlying graph has at most one nontrivial component. 1.4.24 Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory De Bruijn cycles 1.4.25 Application: There are 2n binary strings of length n. Is there a cyclic arrangement of 2n binary digits such that the 2n strings of n consecutive digitals are all distinct? Example: For n =4, (0000111101100101) works. 0000 0001 0011 0111 1111 1110 1101 1011 … 1 Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory De Bruijn cycles 1.4.25 We can use such an arrangement to keep track of the position of a rotating drum. One drum has 2n rotational positions. A band around the circumference is split into 2n portions that can be coded 0 or 1. Sensors read n consecutive portions. If the coding has the property specified above, then the position of the drum is determined by the string read by the sensors. Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory De Bruijn cycles 1.4.25 To obtain such a circular arrangement, define a digraph Dn whose vertices are the binary (n-1)-tuples. Put an edge from a to b if the last n-2 entries of a agree with the first n-2 entries of b. Label the edge with the last entry of b. Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory De Bruijn cycles 1.4.25 Below we show D4.. Put an edge from a to b if the last n-2 entries of a agree with the first n-2 entries of b. Label the edge with the last entry of b. 1 1 001 000 100 010 101 011 110 111 a b Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory De Bruijn cycles 1.4.25 We next prove that Dn is Eulerian and show how an Eulerian circuit yields the desired circular arrangement. Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Theorem. The digraph Dn of Application 1.4.25 is Eulerian, and the edge labels on the edges in any Eulerian circuit of Dn form a cyclic arrangement in which the 2n consecutive segments of length n are distinct. 1.4.26 Proof: We show first that Dn is Eulerian. Then the labels on the edges in any Eulerian circuit of Dn form a cyclic arrangement in which the 2n consecutive segments of length n are distinct. Ch. 1. Fundamental Concept

Theorem. The digraph Dn is Eulerian 1.4.26 Graph Theory Theorem. The digraph Dn is Eulerian 1.4.26 Proof: 1/2 Every vertex has out-degree 2 because we can append a 0 or a 1 to its name to obtain the name of a successor vertex. Similarly, every vertex has in-degree 2, because the same argument applies when moving in reverse and putting a 0 or a 1 on the front of the name. 001 011 101 111 110 Ch. 1. Fundamental Concept

Theorem. The digraph Dn is Eulerian 1.4.26 Graph Theory Theorem. The digraph Dn is Eulerian 1.4.26 Proof: 2/2 Also, Dn is strongly connected, because we can reach the vertex b=(b1,…..,bn-1) from any vertex by successively follows the edges labeled b1,…..,bn-1. Thus Dn satisfies the hypotheses of Theorem 1.4.24 and is Eulerian. a 011 b 001 1 1 1 1 1 1 1 000 010 101 111 1 1 100 110 Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Theorem. The labels on the edges in any Eulerian circuit of Dn form a cyclic arrangement in which the 2n consecutive segments of length n are distinct. 1.4.26 Proof: 1/4 Let C be an Eulerian circuit of Dn. Arrival at vertex a=(a1,…..,an-1) must be along an edge with label an-1 because the label on an edge entering a vertex agrees with the last entry of the name of the vertex. a 001 011 b 1 1 1 1 1 1 000 010 101 111 1 1 100 110 Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Theorem. The labels on the edges in any Eulerian circuit of Dn form a cyclic arrangement in which the 2n consecutive segments of length n are distinct. 1.4.26 Proof: 2/4 The successive earlier labels (looking backward) must have been an-2,…..,a1 in order. because we delete the front and shift the reset to obtain the reset of the name at the head a 001 0 1 1 b 1 1 1 1 1 1 000 010 101 111 1 1 100 110 Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Theorem. The labels on the edges in any Eulerian circuit of Dn form a cyclic arrangement in which the 2n consecutive segments of length n are distinct. 1.4.26 Proof: 2/4 If C next uses an edge with label an, then the list consisting of the n most recent edge labels at that time is a1,…..an. a 001 011 b 1 1 1 1 1 000 1 1 010 101 111 1 1 1 100 110 Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Theorem. The labels on the edges in any Eulerian circuit of Dn form a cyclic arrangement in which the 2n consecutive segments of length n are distinct. 1.4.26 Proof: 3/4 Since the 2n-1 vertex labels are distinct, and the two out-going edges have distinct labels, and we traverse each edge exactly once Distinct vertex label 011 0 011 1 Distinct labels on out-going edges Ch. 1. Fundamental Concept

Ch. 1. Fundamental Concept Graph Theory Theorem. The labels on the edges in any Eulerian circuit of Dn form a cyclic arrangement in which the 2n consecutive segments of length n are distinct. 1.4.26 Proof: 4/4 We have shown that the 2n strings of length n in the circular arrangement given by the edge labels along C are distinct. Ch. 1. Fundamental Concept