Graph Traversal Code - BFS on Graph represented using Adjacency List

 Click Me
import java.util.*;

// A graph node 
class GraphNode{
    String name;
    // this is a list of adjacent nodes for the current node and is  used in adjacency list
    ArrayList<GraphNode> neighbours;
    
    // constructor
    GraphNode(String name){
        this.name = name;
        this.neighbours = new ArrayList<>();
    }
}

public class Main {
    
    // The node list of graph , also used to maintain adjacency list
    ArrayList<GraphNode> nodeList;
    
    // constructor
    Main(){
        this.nodeList = new ArrayList<>();
    }
    
    // function to add an undirected and unweighted edge between two nodes with index i and j in nodeList
    public void addUndirectedEdge(int i, int j){
        GraphNode node1 = this.nodeList.get(i);
        GraphNode node2 = this.nodeList.get(j);
        this.nodeList.get(i).neighbours.add(node2);
        this.nodeList.get(j).neighbours.add(node1);
    }
    
    
    // function to do BFS Traversal on Graph
    // Time Complexity - O(V+E)  Space Complexity - O(V+E)
    public void bfs(){
        // using hashset to keep track of visited nodes is better as compared to using flags
        // If we use flags, we should reset all the visited flags to false for all the nodes after one bfs iteration.
        // But in hashset we don not need to do anything like thing as a new hashset is created for every iteration of bfs
        System.out.println("Traversing Graph using BFS :");
        HashSet<GraphNode> visitedNodes = new HashSet<GraphNode>();
        for(GraphNode graphNode : this.nodeList){
            if(!visitedNodes.contains(graphNode)){
                bfsVisit(graphNode,visitedNodes);
            }
        }
    }
    
    // helper function for bfs
    private void bfsVisit(GraphNode graphNode, HashSet<GraphNode> visitedNodes){
        
        Queue<GraphNode> q = new LinkedList<>();
        q.add(graphNode);
        visitedNodes.add(graphNode);
        while(!q.isEmpty()){
            GraphNode curr = q.poll();
            System.out.print(curr.name+" ");
            
            for(GraphNode neighbour : curr.neighbours){
                if(!visitedNodes.contains(neighbour)){
                    q.add(neighbour);
                    visitedNodes.add(neighbour);
                }
            }
        }
    }
    
    public static void main(String[] args) throws Exception {
        
        Main graph = new Main();
        
        
        // Add 6 nodes to nodes list
        
        for(int i=0;i<6;i++){
            graph.nodeList.add(new GraphNode("V"+i));
        }
        
        
        // add adjancent nodes of each node
        
        /* undirected and Unweighted Graph used for this code  :
                     V0
                   /  \
                  V1   V2
                 / \   |
                |   \ /
               V3----V4
                \    /
                 \  /
                  V5
                  
*/
// add adjancent nodes of each node
        graph.addUndirectedEdge(2,4);
        graph.addUndirectedEdge(0,1);
        graph.addUndirectedEdge(1,3);
        graph.addUndirectedEdge(0,2);
        graph.addUndirectedEdge(3,4);
        graph.addUndirectedEdge(4,5);
        graph.addUndirectedEdge(3,5);
        graph.addUndirectedEdge(1,4);
        
        // Now as we have added all the edges or adjacent nodes in adjacency list [the neighbour ArrayList of each node],
        // we are ready to traverse the graph
        
        graph.bfs();
        
    }
}

Output :

Traversing Graph using BFS :
V0 V1 V2 V3 V4 V5

Comments

Post a Comment

Popular posts from this blog

Binary Heap - Introduction

Image
#Image Credit - GeeksForGeeks Types of heap: Min Heap - every node is less than or equal to  its children(s) Max Heap - every node is more than or equal to its children(s) Operations on any heap : create insert extract/delete - we can extract/delete only the top node in any heap traverse delete entire heap get size of heap peek top of heap We should prefer implementing binary heap using arrays and not linked list, as during bottom to top heapify, in reaching the bottom rightmost node we take only 0(1) time in case of array but we take 0(n) time in case of linked list because first, we need to level order traverse the binary heap tree to get bottom-most rightmost element.  Heaps are used to efficiently implement:  Prim's Algorithm Heapsort Priority queue
Read more

Divide and Conquer - Introduction

Image
What is Divide and Conquer? Divide and Conquer is an algorithm paradigm in which we break down a given problem into smaller sub-problems of similar type until the sub-problems are small enough to be solved directly. The solution to these sub-problems are then combined to give a solution to the original problem.  Study the diagram below, it will then make more sense about what we do in Divide and Conquer : Where to Apply Divide and Conquer? The Divide and Conquer can be applied to all those problems that show Optimal Substructure property.  So, What is this Optimal Substructure property now? If we can break down a problem into similar sub-problems and the solution of these sub-problems can be used to create the solution of the whole problem, then that problem is said to have the Optimal Substructure property. For eg. : Fibonacci(n) = Fibonacci(n-1) + Fibonacci(n-2),    In this example, we can see that if we want to find the Fibonacci number at the nth place, we can br...
Read more

Bubble Sort

Time complexity - O(n^2)  || Space Complexity - O(1) Bubble sort is an inplace sorting algorithm, that means we do not any extra space while bubble sorting public class Main {     public static void main(String[] args) throws Exception {         int[] arr={1,4,4,5,6,2,3,90,120,43,0};                  System.out.println("UnSorted Array :");         for(int i=0;i<arr.length;i++){             System.out.print(arr[i]+" ");         }         System.out.println();         // bubble sort         for(int i=0;i<arr.length-1;i++){ // iterates through first to last cell             for(int j=0;j<arr.length-1-i;j++){ // inner loop controls swapping and extent to which swapping is to be performed in array     ...
Read more