E.G.M. PetrakisGraphs1 Graph G(V,E): finite set V of nodes (vertices) plus a finite set E of arcs...

E.G.M. Petrakis Graphs 1

Graphs 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

BG

C

E

F

D


Terminology Two vertices are adjacent if they are

connected with an edge Adjacent or 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, edges of G Complete graph: contains all possible

edges |E| in Θ(|V|2)


Undirected Graphs

The edges are not directed from one vertex to another

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B

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D


Directed Graphs

The edges are directed from one vertex to another

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B C

FD

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B C


More Definitions

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>| x, y ∊ N} x < y x mod y = odd

Labeled graph: labels on vertices or edges

Weighted graph: weights on edges


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3 10

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relation

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weighted


Paths

There exist edges connecting two nodes 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


Connected Graphs

An undirected graph is connected if there is a path between any two vertices

Connected components: maximally connected subgraphs

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43

2 6

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Operations on Graphs

join (a,b) join (a,b,x) remv[wt] (a,b,x) adjacent (a,b)

a b a b

a b a bx

a b

c

adjacent (a,b) = trueadjacent (a,c) = false

a b a b


Build a Graph

read(n); // number of nodesread and create n nodeslabel them from 1 .. n

while not eof(..) {

read(node1, node2, w12);joinwt(node1, node2, w12);

}

any graph algorithm


Representation of Directed Graphs

0 2

4

31

directed graph

111

11

1

3 4210

01234

adjacency matrix1 4

3

4

2

14

3

2

0

1

adjacency list


Representation of Undirected Graphs

0 2

4

31

undirected graph

adjacency list

1 4

0

3

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04

3

2

0

1 3

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2

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4

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adjacency matrix

3 4210

01234

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1 11

11 1

1


Matrix Implementation

Simple but difficult to process edges, unused space in matrix

mark: 1D array marking vertices mark[i] = 1 if vertex i has been visited

typedef int *Edge; matrix: the actual Graph implemented

as an 1D array of size n2

edge (i, j) is found at matrix[i*n + j] if edge exists matrix[i*n + j] = wt otherwise matrix[i*n + j] = NOEDGE


Graph class: Adjacency Matrix

template <class Edge> class Graph {private:

Edge* matrix; // the edge matrixint numVertex, numEdge; // number of vertices, edgesbool* Mark; // the mark array

public:Graph( ); ~Graph( ); // constructor, destructorint n( ), e ( ) // number of vertices, edgesEdge first(int); // get the first edge for a vertexbool isEdge(Edge); // TRUE if this is an edgeEdge next(Edge); // get next edge for a vertexint v1(Edge), v2(Edge) // vertex edge comes from, goes toint weight(int, int); // return weight of edgeint weight(Edge); // return weight of edge

};


Constructor, Destructor

template <class Edge>Graph<Edge>::Graph( ) {

mark = NULL; matrix = NULL;}

template <class Edge>Graph<Edge>::~Graph( ) {

if (mark != NULL) delete [ ] mark;if (matrix != NULL) delete [ ] matrix;

}


Member Functions

template <class Edge>int Graph<Edge>::n( ) { return numVertex; }

template <class Edge>int Graph<Edge>::e( ) { return numEdge; }


Member Functions (cont.)

template <class Edge> Edge Graph<Edge>::first(int v) { //first edge

of nodeint stop = (v+1) *numVertex; // pos. 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;

}



template <class Edge>bool Graph<Edge>::isEdge(Edge w)

{ return w != NULL; }

template <class Edge>Edge Graph<Edge>::next(Edge w) { // next edge of

(w,0)int stop = (v1(w) + 1) * numVertex; // pos at end of rowfor (int pos = (w – matrix) + 1; pos < stop; pos++)

if (matrix[pos] != NOEDGE) return &matrix[pos];return NULL;

}



template <class Edge>int Graph<Edge>::v1(Edge w) // 1st vertex of

edge { return ( w – matrix ) / numVertex; }

template <class Edge>int Graph<Edge>::v2(Edge w) // 2nd vertex edge

{ return ( w – matrix ) % numVertex; }



template <class Edge>int Graph<Edge>::weight( int i, int j) { // return

weight of edgeif ( matrix[ i * numVertex + j ] == NOEDGE )

return INFINITY;else return matrix[ i * numVertex + j ]; }

template <class Edge>int Graph<Edge>::weight(Edge w) { // return weight of

edgeif ( *w == NOEDGE ) return INFINITY;else return *w;

}


Graph Class: Adjacency List

template <class Edge> class Graph {private:

Edge *list; // the vertex listint numVertex; // number of verticesint numEdge; // number of edgesbool *Mark; // the mark array

public:Graph( ); ~Graph( ); // constructor, destructorint n( ), e( ); // number of vertices, edgesEdge first(int); // get the first edge of vertexbool isEdge(Edge); // TRUE if this is an edgeEdge next(Edge); // get next edge for a vertexint v1(Edge), v2(Edge); // return vertex edge comes from, goes toint v2(Edge); // return vertex edge goes toint weight(int, int), // return weight of edgeint weight(weight); // return weight of edge

};


A Singly-Linked List Node class EdgeLink {

public:int weight; // edge weightint v1; // 1st vertex of edgeint v2; // 2nd vertex of edgeEdgeLink *next; // pointer to next edge in listEdgeLink(int vt1, int vt2, int w, EdgeLink *nxt = NULL)

// constructor{ v1 = vt; v2 = vt2; weight = w; next = nxt; }

};

Graph<EdgeLink> g;


Member Functionstemplate <class Edge>Graph<Edge>::Graph( ) { // constructor

Mark = NULL; list = NULL;}template <class Edge>Graph<Edge>::~Graph( ) { // destructor: return

allocated spaceif (Mark != NULL) delete [ ] Mark;if (list != NULL) { // remove all of the edges for (int v = 0; v < n( ); v++) // for each vertex return all edges

while (list[v] != NULL) { list[v]= list[v]next; delete temp;

} delete [ ] list; // remove the vertex list headers}

}


Member Functions (cont.)template <class Edge> int Graph<Edge>::n( ) { return numVertex; } // number of

vertices

template <class Edge>int Graph<Edge>::e( ) { return numEdge; } // number of

edges

template <class Edge>Edge Graph<Edge>::first(int v) // get the first edge

{return list[v]; }

template <class Edge>bool Graph<Edge>::isEdge(Edge w) // TRUE if it is

an edge{ return w != NULL; }


Member Functions (cont.)template <class Edge>Edge Graph<Edge>::next(Edge w) { // get next edge for a

vertexif (w == NULL) return NULL;else return wnext;

}

template <class Edge>int Graph<Edge>::v1(Edge w) // return vertex edge comes

from{ return wv1; }

template <class Edge>int Graph<Edge>::v2(Edge w) // return vertex edge goes to

{ return wv2; }



template <class Edge>int Graph<Edge>::weight( int i, int j) { // return

weight of edgefor (Edge curr = list[i]; curr != NULL; curr = curr->next)

if (currv2 == j ) return curr-> weight;return INFINITY;

}

template <class Edge>int Graph<Edge>::weight(Edge w) { // return weight of

edgeif ( w == NULL ) return INFINITY;else return w -> weight;

}


A Better Implementation

ndptr: pointer to adjacent nodenextarc: pointer to next edge

arcptr info nextnode

info: dataarcptr: pointer to an adjacent nodenextnode: pointer to a graph node

ndptr nextarc

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A Βnil

C D Enil

nil

nil

nil

nil

<C,E>

<A,E><A,D><A,C><A,B>

<D,B>

graph


Graph Traversal

Trees preorder inorder postorder

Graphs Depth First Search (DFS) Breadth First Search (BFS)


Depth First Search (DFS)

Starting from vertex v, initially all vertices are “unvisited”

void DFS(v) { Mark(v) = true; for each vertex w adjacent to v if (!Mark(w)) DFS(w)

}


Complexity of DFS

O(|V| + |E|): adjacency list representation O(|V|2) in dense graphs

O(|V|2): matrix representation


DFS : V1V2V4V8V5V6V3V7

v = v1

v1

v2 v3

v4 v5 v6 v7

v8

v3

v2

v1

v6

v5

v4

v8

v7

21122334

34688885

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76


Breadth-First Search (BFS)

ΒFS : V1V2V3V4V5V6V7V8

v = v1

v1

v2 v3

v4 v5 v6v7

v8


BFS (cont.) Starting from vertex v, all nodes “unvisited”,

visit adjacent nodes (use a queue)void DFS(v) {

visited(v) = true;enqueue(v, Q);while ( Q ≠ 0 ) {

x = dequeue(Q);for each y adjacent xdo if ! Mark(y) { Mark(y) = TRUE;

enqueue(y,Q);}

}}


v1front

rearv3v2front

rear

output(v1)

v3front

rear

v5v4 output(v2)

output(v3)v4front

rear

v7v5 v6

v5front

rear

v8v6 v7 output(v4)

v6front v7 v8 output(v5)

v6front v8output(v6)

output(v7)

v = v1 v1

v2 v3

v4 v5 v6 v7

v8


Complexity of BFS

O(|V|+|E|) : adjacency list representation

d1 + d2 + ...+ dn = |E| di = degree (vi) O(|V|2) : adjacency matrix

representation


DFS Algorithm

template <class Edge>void DFS (Graph<Edge>& G, int v) { action(G,v);

G.Mark[v] = VISITED;for ( Edge w = G.first(v); G.isEdge(w); w = G.next(w))

if (G.Mark[G.v2(w)] = = UNVISITED) DFS ( G, G.v2(w));action(G,v);

}


BFS Algorithmtemplate <class Edge>void BFS (Graph<Edge>& G, int start) {

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.v2(w)] = = UNVISITED) { G.Mark[G.v2(w)] = VISITED;

Q.enqueue(G.v2(w)); }

action(G,v);}


Connected - Unconnected

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B C

D

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connected components

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AB

CD

connected graph: undirected graph, there is a path connecting any two nodes

unconnected graph: not always a path


Connected Components

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|)


Spanning Trees

(G,E)

Tree formed from edges and nodes of G

Spanning Tree


Spanning Forest

Set of disjoint spanning trees Ti of G=(V,E) Ti = ( Vi , Ei ) 1≤ i ≤ k, Vi,Ei:subsets of V, E DFS produces depth first spanning trees or

forest BFS breadth first spanning trees or forest Undirected graphs provide more traversals

produce less but short spanning trees Directed graphs provide less traversals

produce more and higher spanning trees


DFS

DFS spanning tree

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DFS

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DFS

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E GF

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ADFS spanning forest


BFS

BFS spanning treeΒFS

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BFS spanning forestC

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ΒFS


Min. Cost Spanning Tree

costT = 1 + 5 + 2 + 4 + 3

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51Prim’s algorithm: Complexity O(|V|2)


Prim’s Algorithmtemplate <class Edge>void Prim(Graph<Edge>& G, int s) { / /prim’s MST algorithm

int D[G.n( )]; // distance vertexint V[G.n( )]; // who’s closestfor (int i=0; i<G.n( ); i++); D[i] = INFINITY; // initializeD[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 MSTif (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 }

}}


Prim’s Algorithm (cont.)

template <class Edge> ){ // find min cost vertexvoid minVertex(Graph<Edge>& G, int* D

int v;for (int i = 0; i < G.n( ); i++) // initialize

if (G.Mark[i] = = UNVISITED) { v = i; break; }for ( i=0; i<G.n( ); i++) // find smallest valueif ((G.Mark[i] = = UNVISITED) && ( D[i] < D[v] ))

v = i;return v;

}


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


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starting point: v = 1

step S W D(2) D(3) D(4) D(5)

1 {1} - 10 infinite 30 100

2 {1,2} 2 10 60 30 100

3 {1,2,4} 4 10 50 30 90

4 {1,2,4,3} 3 10 50 30 60

5 {1,2,4,3,5}

5 10 50 30 60


Dijkstra’s Algorithm (cont.)

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


Compute Shortest Pathtemplate <class Edge>void Dijkstra(Graph<Edge>& G, int s) { 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); }}


Find Min. Cost Vertex

template <class Edge>int minVertex(Graph<Edge>& G, int* D) { int v; for (int i = 0; i < G.n( ); i++) if (G.getMark(i) == UNVISITED) { v = i; break; } for (i++; i < G.n( ); i++) // find smallest D

value if ((G.getMark(i) == UNVISITED) && (D[i] < D[v])) v = i; return v;}

minVertex: scans through the list of vertices for the min. value O(|V|) time

Complexity: O(|V|2 + |E|) = O(|V|2) since O(|E|) is O(|V|2)

E.G.M. PetrakisGraphs1 Graph G(V,E): finite set V of nodes (vertices) plus a finite set E of arcs...

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