I am unsure as to how to use BFS to find all shortest paths between two vertices in a given graph in C. The graph is unweighted and undirected. So far I have the following BFS code (not sure how to turn it to finding all shortest paths between two given nodes) . I am not sure how to find all of them and store them. Help would be much appreciated.
// BFS algorithm
void bfs(struct Graph* graph, int startVertex) {
struct queue* q = createQueue();
graph->visited[startVertex] = 1;
enqueue(q, startVertex);
while (!isEmpty(q)) {
printQueue(q);
int currentVertex = dequeue(q);
printf("Visited %d\n", currentVertex);
struct node* temp = graph->adjLists[currentVertex];
while (temp) {
int adjVertex = temp->vertex;
if (graph->visited[adjVertex] == 0) {
graph->visited[adjVertex] = 1;
enqueue(q, adjVertex);
}
temp = temp->next;
}
}
}
below is my full program:
// BFS algorithm in C
#include <stdio.h>
#include <stdlib.h>
#define SIZE 40
struct queue {
int items[SIZE];
int front;
int rear;
};
struct queue* createQueue();
void enqueue(struct queue* q, int);
int dequeue(struct queue* q);
void display(struct queue* q);
int isEmpty(struct queue* q);
void printQueue(struct queue* q);
struct node {
int vertex;
struct node* next;
};
struct node* createNode(int);
struct Graph {
int numVertices;
struct node** adjLists;
int* visited;
};
// BFS algorithm
void bfs(struct Graph* graph, int startVertex) {
struct queue* q = createQueue();
graph->visited[startVertex] = 1;
enqueue(q, startVertex);
while (!isEmpty(q)) {
printQueue(q);
int currentVertex = dequeue(q);
printf("Visited %d\n", currentVertex);
struct node* temp = graph->adjLists[currentVertex];
while (temp) {
int adjVertex = temp->vertex;
if (graph->visited[adjVertex] == 0) {
graph->visited[adjVertex] = 1;
enqueue(q, adjVertex);
}
temp = temp->next;
}
}
}
// Creating a node
struct node* createNode(int v) {
struct node* newNode = malloc(sizeof(struct node));
newNode->vertex = v;
newNode->next = NULL;
return newNode;
}
// Creating a graph
struct Graph* createGraph(int vertices) {
struct Graph* graph = malloc(sizeof(struct Graph));
graph->numVertices = vertices;
graph->adjLists = malloc(vertices * sizeof(struct node*));
graph->visited = malloc(vertices * sizeof(int));
int i;
for (i = 0; i < vertices; i ) {
graph->adjLists[i] = NULL;
graph->visited[i] = 0;
}
return graph;
}
// Add edge
void addEdge(struct Graph* graph, int src, int dest) {
// Add edge from src to dest
struct node* newNode = createNode(dest);
newNode->next = graph->adjLists[src];
graph->adjLists[src] = newNode;
// Add edge from dest to src
newNode = createNode(src);
newNode->next = graph->adjLists[dest];
graph->adjLists[dest] = newNode;
}
// Create a queue
struct queue* createQueue() {
struct queue* q = malloc(sizeof(struct queue));
q->front = -1;
q->rear = -1;
return q;
}
// Check if the queue is empty
int isEmpty(struct queue* q) {
if (q->rear == -1)
return 1;
else
return 0;
}
// Adding elements into queue
void enqueue(struct queue* q, int value) {
if (q->rear == SIZE - 1)
printf("\nQueue is Full!!");
else {
if (q->front == -1)
q->front = 0;
q->rear ;
q->items[q->rear] = value;
}
}
// Removing elements from queue
int dequeue(struct queue* q) {
int item;
if (isEmpty(q)) {
printf("Queue is empty");
item = -1;
} else {
item = q->items[q->front];
q->front ;
if (q->front > q->rear) {
printf("Resetting queue ");
q->front = q->rear = -1;
}
}
return item;
}
// Print the queue
void printQueue(struct queue* q) {
int i = q->front;
if (isEmpty(q)) {
printf("Queue is empty");
} else {
printf("\nQueue contains \n");
for (i = q->front; i < q->rear 1; i ) {
printf("%d ", q->items[i]);
}
}
}
int main() {
struct Graph* graph = createGraph(6);
addEdge(graph, 0, 1);
addEdge(graph, 0, 2);
addEdge(graph, 1, 2);
addEdge(graph, 1, 4);
addEdge(graph, 1, 3);
addEdge(graph, 2, 4);
addEdge(graph, 3, 4);
bfs(graph, 0);
return 0;
}
CodePudding user response:
Dijkstra's algorithm is closely related to BFS, and the two are equivalent for uniformly weighted graphs. It should come as no surprise, then, that the same kind of adaptation that one makes to Dijkstra's algorithm to capture the shortest path in addition to that path's length can be used with a BFS to find fewest-node paths.
Specifically,
- You annotate the starting node(s) with a null preceding node.
- Each time you enqueue another node to traverse, you annotate it with the node from which you reached it as its preceding node.
Then, when you reach a target node, you can obtain a path by tracing the chain of preceding nodes. If you have written your BFS itself correctly then the resulting paths will be optimal, but not necessarily unique.
To get all the optimal paths, you can use a further adaptation of the above. Instead of tracking a single predecessor per node, track a list of predecessors that are all the same distance from a start node. This requires that you also track each node's distance (again like Dijkstra's algorithm). Then, when you are considering the successors to a node you are visiting, you test them, including those that have already been visited, to determine whether to add the current node to their lists of predecessors.
You must then continue the BFS procedure until one of the following is true:
- there are no more nodes in the queue, OR
- all the target nodes have been visited, and all enqueued nodes are at least as far from a start node as the farthest target node (but you only need to check the head node of the queue, as none of the later ones can be closer than it)
At this point, all the optimum paths can be obtained by tracing chains of preceding nodes. Note that there is an analog of this for Dijkstra's algorithm, too.