1. E.G.M. PetrakisGraphs1 Graph G(V,E): finite set V of nodes (vertices) plus a finite set E of arcs (or edges) between nodes –V = {A, B, C, D, E, F, G} –E = {AA, AB, AD, CD, DE, BF, EF, FG, EG } A B G C E F D

2. E.G.M. PetrakisGraphs2 Terminology Two vertices are adjacent if they are connected with an edge –Neighbor vertices Subgraph of G=(V,E): a graph G’=(V’,E’) where V’, E’ are subsets of V, E |V|, |E|: number of nodes and edges of G Complete graph: contains all possible edges – |E| in Θ(|V| 2 )

3. E.G.M. PetrakisGraphs3 G E H A B C F D Undirected graphs: the edges are not directed from one vertex to another

4. E.G.M. PetrakisGraphs4 Directed Graphs: the edges are directed G E H A B C F D

5. E.G.M. PetrakisGraphs5 G E A BC FD A D BC

6. E.G.M. PetrakisGraphs6 Vertex degree : maximum number of incoming or outgoing edges. –In-degree: number of incoming edges –Out-degree: number outgoing edges Relation R on A: R = { | x,y ∊ N} –x < y –x mod y = odd Labeled graph : labels are associated to vertices or edges Weighted graph: weights on edges

7. E.G.M. PetrakisGraphs7 8 310 6 17 5 Relation 8 310 6 17 5 1 7 13 1 5 Weighted

8. E.G.M. PetrakisGraphs8 Vertices (V 1,V 2,…V n ) form a path if there exist edges from V i to V i+1 for 1<= i <= n –V 1,V 2,…V n : adjacent –Length of path : number of edges –Weighted graphs: sum of weights on path –Cycle: path (of length 3 or more) connecting a vertex with itself – Cyclic graph : contains cycle – Acyclic graph : does not contain cycle – DAG: Directed Acyclic Graph

9. E.G.M. PetrakisGraphs9 An undirected graph is connected if there is at least one path between any two vertices Connected components: maximally connected subgraphs Free tree of graph: connected, undirected, acyclic subgraph of the graph –A free tree has |V| nodes and |V|-1 edges 0 1 4 3 26 5 7

10. E.G.M. PetrakisGraphs10 join (a,b) join (a,b,x) remv[wt] (a,b,x) adjacent (a,b) ab ab ab ab x ab ab x a b c adjacent (a,b) = true adjacent (a,c) = false Operations on graphs

11. E.G.M. PetrakisGraphs11 Build Graphs read(n);// number of nodes read and create n nodes label them from 1.. n While not eof(..) { read(node 1, node 2, w 12 ); joinwt(node 1, node 2, w 12 ); } any graph algorithm

12. E.G.M. PetrakisGraphs12 Representation of Directed Graphs 02 4 31 Directed Graph 11 1 1 1 1 34210 0 1 2 3 4 adjacency matrix 14 3 4 2 1 4 3 2 0 1 adjacency list

13. E.G.M. PetrakisGraphs13 Representation of Undirected Graphs 02 4 31 Undirected Graph Adjacency List 14 0 3 1 0 4 3 2 0 1 3 4 2 1 4 2 Adjacency Matrix 34210 0 1 2 3 4 1 1 1 1 1 11 1 1 1 1 1

14. E.G.M. PetrakisGraphs14 Graph Implementation Matrix representation, Adjacency list –simple implementation –difficult to process edges –unused space in matrix auxiliary functions and structures –Mark: 1D array marking vertices Mark[i] = 1 if vertex i has been processed (visited) –typedef int * Edge; –matrix: the actual Graph implemented as an 1D array of size n 2 edge (i, j) is found at matrix[i * n + j] if edge exists matrix[i * n + j] = wt otherwise matrix[i * n + j] = NOEDGE

15. E.G.M. PetrakisGraphs15 Class Graph {// Graph class: Adjacency matrix private: int* matrix;//The edge matrix int numVertex;//Number of vertices int numEdge;//Number of edges bool* Mark;//The mark array public: Graph( );//Constructor ~Graph( );//Destructor int n( );//Number of vertices for graph int e( );//Number of edges for graph Edge first(int);//Get the first edge for a vertex bool isEdge(Edge);//TRUE if this is an edge Edge next(Edge);//Get next edge for a vertex int v1(Edge);//Return vertex edge comes from int v2(Edge);//Return vertex edge goes to int weight(int, int);//Return weight of edge int weight(Edge);//Return weight of edge }

16. E.G.M. PetrakisGraphs16 Graph::Graph( ) {//Constructor Mark = NULL; matrix = NULL;//plus code for creating graph } Graph::~Graph( ) {//Destructor: return allocated space if (Mark != NULL) delete [ ] Mark; if (matrix != NULL) delete [ ] matrix; } int Graph::n( ) { return numVertex; } //Num. of vertices for the graph int Graph::e( ) //Number of edges { return numEdge; } Edge Graph::first(int v) {//Get the first edge for a vertex int stop = (v+1) * numVertex;//Position at end of v’s row for ( int pos = v * numVertex; pos < stop; pos++) //scans entire row if ( matrix[pos] != NOEDGE) return &matrix[pos]; return NULL; }

17. E.G.M. PetrakisGraphs17 bool Graph::isEdge(Edge w)//TRUE if (w,0) is an edge { return w != NULL; } Edge Graph::next(Edge w) {//Get next edge of (w,0) int stop = (v1(w) + 1) * numVertex;//Position at end of row for (int pos = (w – matrix) + 1; pos < stop; pos++) if (matrix[pos] != NOEDGE)return &matrix[pos]; return NULL; } int Graph::v1(Edge w)//Return 1 st vertex of edge { return ( w – matrix ) / numVertex; }//Pointer arithmetic int Graph::v2(Edge w)//Return 2 nd vertex edge { return ( w – matrix ) % numVertex; }//Pointer arithmetic int Graph::weight( int i, int j) {//Return weight of edge if ( matrix[ i * numVertex + j ] = = NOEDGE ) return INFINITY; else return matrix[ i * numVertex + j ]; } int Graph::weight(Edge w) {//Return weight of edge if ( * w = = NOEDGE ) return INFINITY; else return * w; }

18. E.G.M. PetrakisGraphs18 Class Graph {// Graph class: Adjacency list private: Edge * list;//The vertex list int numVertex;//Number of vertices int numEdge;//Number of edges bool * Mark;//The mark array public: Graph( );//Constructor ~Graph( );//Destructor int n( );//Number of vertices for graph int e( );//Number of edges for graph Edge first(int);//Get the first edge for a Vertex bool isEdge(Edge);//TRUE if this is an edge Edge next(Edge);//Get next edge for a vertex int v1(Edge);//Return vertex edge comes from int v2(Edge);//Return vertex edge goes to int weight(int, int);//Return weight of edge int weight(weight);//Return weight of edge };

19. E.G.M. PetrakisGraphs19 class EdgeLink {//A singly-linked list node public: int weight;//Edge weight int v1;//1 st vertex of edge int v2; //2 nd vertex of edge EdgeLink * next;//Pointer to next edge in list EdgeLink(int vt1, int vt2, int w, EdgeLink * nxt = NULL) //Constructor { v1 = vt; v2 = vt2; weight = w; next = nxt; } }; Typedef EdgeLink* Edge;

20. E.G.M. PetrakisGraphs20 Graph::Graph( ) {//Constructor Mark = NULL;list = NULL;// the code for creating a graph follows } Graph::~Graph( ) {//Destructor: return allocated space if (Mark != NULL) delete [ ] Mark; if (list != NULL) {//Remove all of the edges for (int v = 0; v < n( ); v++)//For each vertex while (list[v] != NULL) {//return its edges Edge temp = list[v]; list[v]= list[v]  next; delete temp; } delete [ ] list;//Now remove the vertex list headers } int Graph::n( ) { return numVertex; }//Number of vertices int Graph::e( ) { return numEdge; }//Number of edges

21. E.G.M. PetrakisGraphs21 Edge Graph::first(int v)//Get the first edge for a vertex {return list[v]; } bool Graph::isEdge(Edge w)//TRUE if this is an edge { return w != NULL; } Edge Graph::next(Edge w) {//Get next edge for a vertex if (w == NULL) return NULL; else return w  next; } int Graph::v1(Edge w)//Return vertex edge comes from { return w  v1; } int Graph::v2(Edge w)//Return vertex edge goes to { return w  v2; }

22. E.G.M. PetrakisGraphs22 int Graph::weight( int i, int j) {//Return weight of edge for (Edge curr = list[i]; curr != NULL; curr = curr ⇢ next) if (curr  v2 = = j ) return curr  weight; return INFINITY; } int Graph::weight(Edge w) {//Return weight of edge if ( w = = NULL ) return INFINITY; else return w  weight; }

23. E.G.M. PetrakisGraphs23 arcptrinfonextnode info: data arcptr: pointer to an adjacent node nextnode: pointer to a graph node ndptrnextarc ndptr: pointer to adjacent node nextarc: pointer to next edge E A B C D An even better representation

24. E.G.M. PetrakisGraphs24 AΒ nilnil CDE nilnil nilnil nilnil nilnil nilnil graph

25. E.G.M. PetrakisGraphs25 Trees –preorder –inorder –postorder Graphs –Depth first search –Breadth first search Graph Traversals

26. E.G.M. PetrakisGraphs26 Depth First Search: –Starting from vertex v, initially all vertices unvisited void DFS(v) { Mark(v) = true; for each vertex w adjacent to v if (!Mark(w)) DFS(w) } Complexity DFS : –O(|V| + |E|): adjacency list representation O(|V| 2 ) in dense graphs –O(|V| 2 ): matrix representation

27. E.G.M. PetrakisGraphs27 DFS : V 1 V 2 V 4 V 8 V 5 V 6 V 3 V 7 v = v 1 v1v1 v2v2 v3v3 v4v4 v5v5 v6v6 v7v7 v8v8 v3v3 v2v2 v1v1 v6v6 v5v5 v4v4 v8v8 v7v7 2 1 1 2 2 3 3 4 3 4 6 8 8 8 8 5 5 7 7 6

28. E.G.M. PetrakisGraphs28 Breadth-First Search ΒFS : V 1 V 2 V 3 V 4 V 5 V 6 V 7 V 8 v = v 1 v1v1 v2v2 v3v3 v4v4 v5v5 v6v6 v7v7 v8v8

29. E.G.M. PetrakisGraphs29 Breadth First Search (BFS): –Starting from vertex |V|, Initially all nodes are “unvisited” –Visit adjacent nodes of current nodes (use a queue) void DFS(v) { visited(v) = true; enqueue(v, Q); while ( Q ≠ 0 ) { x = dequeue(Q); for each y adjacent x do if ! Mark(y) { Mark(y) = TRUE; enqueue(y,Q); }

30. E.G.M. PetrakisGraphs30 v1v1 front rear v3v3 v2v2 front rear output(v 1 ) v3v3 front rear v5v5 v4v4 output(v 2 ) output(v 3 ) v4v4 front rear v7v7 v5v5 v6v6 v5v5 front rear v8v8 v6v6 v7v7 output(v 4 ) v6v6 front v7v7 v8v8 output(v 5 ) v6v6 front v8v8 output(v 6 ) output(v 7 ) v = v 1 v1v1 v2v2 v3v3 v4v4 v5v5 v6v6 v7v7 v8v8

31. E.G.M. PetrakisGraphs31 Complexity of BFS: –O(|V|+|E|) : adjacency list representation –d 1 + d 2 +...+ d n = |E| –d i = degree (v i ) –O(|V| 2 ) : adjacency matrix representation

32. E.G.M. PetrakisGraphs32 void DFS (Graph& G, int v) {// Depth-First Search action(G,v); G.Mark[v] = VISITED; for ( Edge w = G.first(v); G.isEdge(w); w = G.next(w)) if (G.Mark[G.v 2 (w)] = = UNVISITED) DFS ( G, G.v 2 (w)); action(G,v); } void BFS (Graph& G, int start) {// Breadth-First Search Queue Q(G.n( )); Q.enqueue(start); G.Mark[start] = VISITED; While ( !Q.isempty( )){ int v = Q.dequeue( ); action(G,v); for ( Edge w = G.first(v); G.isEdge(w); w = G.next(w)) if (G.Mark[G.v 2 (w)] = = UNVISITED) { G.Mark[G.v 2 (w)] = VISITED; Q.enqueue(G.v 2 (w)); } action(G,v); }

33. E.G.M. PetrakisGraphs33 connected graphs: undirected graph, there is a path connecting any two nodes unconnected graphs: not always a path A BC D E connected components E F G A B C D

34. E.G.M. PetrakisGraphs34 If there exist unconnected nodes in a DFS or BFS traversal of G then G is unconnected Find all connected components: void COMP(G, n) { for i = 1 to n if Mark(i) == UNVISITED then DFS(i) [or BFS(i)]; } Complexity: O(|V| + |E|)

35. E.G.M. PetrakisGraphs35 Tree formed from edges and nodes of G Spanning Trees Spanning Trees of G : (G,E)

36. E.G.M. PetrakisGraphs36 Spanning forest of G = ( V, E ): set of disjoint spanning trees of G –T i = ( V i, E i ) 1≤ i ≤ k where V i and E i are subsets of V and E DFS produces depth first spanning trees or forest BFS breadth first spanning trees / forest Undirected graphs provide more traversals –produce less but short spanning trees Directed graphs provide less traversals –produce more and higher spanning trees

37. E.G.M. PetrakisGraphs37 DFS DFS spanning tree D BC E A DFS C B DE A C BF E A G D D C EGF B A DFS spanning forest

38. E.G.M. PetrakisGraphs38 BFS BFS spanning tree ΒFS E BC F A D GE BC F A D G BFS spanning forest C BF E A H D C H DE F B A ΒFS

39. E.G.M. PetrakisGraphs39 Minimum Cost Spanning Tree cost T = 1 + 5 + 2 + 4 + 3 3 1 1 3 6 1 4 1 3 6 1 4 42 1 23 6 1 4 5 42 1 5 23 6 1 4 5 3 42 1 5 23 6 1 4 6 5 6 6 3 42 5 5 1 Prim’s algorithm: Complexity O(|V| 2 )

40. E.G.M. PetrakisGraphs40 void Prim(Graph& G, int s) {//Prim’s MST algorithm int D[G.n( )];//Distance vertex int V[G.n( )];//Who’s closest for (int i=0; i<G.n( ); i++); D[i] = INFINITY; //Initialize D[s] = 0; for ( i=0; i<G.n( ); i++){//Process the vertices int v = minVertex(G, D); G.Mark[v] = VISITED; if ( v != s ) AddEdgetoMST(V[v], v);//Add this edge to MST if (D[v] = = INFINITY ) return;//unreachable vertices for ( Edge w = G.first(v); G.isEdge(w); w = G.next(w)) if (D[G.v2(w)] > G.weight(w)) { D[G.v2(w)] = G.weight(w);//Update distance and V[G.v2(w)] = v;// who it came from }

41. E.G.M. PetrakisGraphs41 void minVertex(Graph& G, int* D) {//Find min cost vertex int v; //Initialize v to any unvisited vertex for (int i = 0; i < G.n( ); i++)//Initialize if (G.Mark[i] = = UNVISITED){ v = i; break; } for ( i=0; i<G.n( ); i++)//Now find smallest value if ((G.Mark[i] = = UNVISITED) && ( D[i] < D[v] )) v = i; return v; }

42. E.G.M. PetrakisGraphs42 Dijkstra’s algorithm: find the shortest path from a given node to every other node in a graph G –No better algorithm for single ending node –Notation: G = (V,E) : input graph C[i,j] : distance between nodes i, j V : starting node S : set of nodes for which the shortest path from v has been computed D(W) : length of shortest path from v to w passing through nodes in S

43. E.G.M. PetrakisGraphs43 20 1 2 3 5 4 100 10 60 50 10 30 starting point: v = 1 stepSWD(2)D(3)D(4)D(5) 1{1}-10 infinite 30100 2{1,2}2106030100 3{1,2,4}410503090 4{1,2,4,3}310503060 5{1,2,4,3,5}510503060

44. E.G.M. PetrakisGraphs44 function Dijkstra(G: graph, int v) { S = {1}; for i = 2 to n D[i] = C[i,j]; while (S != V) { choose w from V-S: D[w] = minimum S = S + {w}; for each v in V–S: D[v] = min{D[v], D[w]+[w,v]}*; } * If D[w]+C[w,v] < D[v] then P[v] = w: keep path in array

45. E.G.M. PetrakisGraphs45 void Dijkstra(Graph& G, int s) { // Compute shortest path distances int D[G.n( )]; for (int i=0; i < G.n( ); i++) // Initialize D[i] = INFINITY; D[s] = 0; for (i = 0; i < G.n( ); i++) { // Process the vertices int v = minVertex(G, D); if (D[v] == INFINITY) return; // Remaining vertices unreachable G.setMark(v, VISITED); for (Edge w = G.first(v); G.isEdge(w); w = G.next(w)) if (D[G.v2(w)] > (D[v] + G.weight(w))) D[G.v2(w)] = D[v] + G.weight(w); }

46. E.G.M. PetrakisGraphs46 int minVertex(Graph& G, int* D) { // Find min cost vertex int v; // Initialize v to any unvisited vertex; for (int i = 0; i < G.n( ); i++) if (G.getMark(i) == UNVISITED) { v = i; break; } for (i++; i < G.n( ); i++) // Now find smallest D value if ((G.getMark(i) == UNVISITED) && (D[i] < D[v])) v = i; return v; } minVertex: scans through the list of vertices searching for the min value  O(|V|) time Complexity : O(|V| 2 + |E|) = O(|V| 2 ) since O(|E|) is O(|V| 2 )