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The methods of traversing to search a graph are BFS (Breadth-first search) or DFS (Depth-first search). The method of visiting all of the nodes in a graph is known as graph traversal. A graph would be made up of Vertices (V) and Edges (E) that connect the vertices together.
The main difference between BFS and the DFS would be that BFS goes by levels, whereas DFS takes a path from the beginning to the end vertex, and then it takes another route from the end to the beginning, again and again until all vertices are properly visited. Moreover, BFS stores the nodes in a queue, whereas DFS traverses the nodes using a stack.
BFS and Its applications
BFS is an algorithm for graphing data, searching trees, and traversing structures. The algorithm visits all of the key vertices in a graph in an efficient and accurate breadthwise manner and marks it. This algorithm visits all nodes adjacent to a single vertex in a graph. After visiting and marking the starting vertex, the algorithm moves on to the next unvisited node and analyses it. All vertices are marked once they’ve been visited. These iterations will continue until all of the graph’s nodes have been visited and marked successfully. BFS stands for breadth-first search in its full form. Here are some BFS applications:Unweighted Graphs
The BFS algorithm can easily generate the shortest distance as well as a minimum spanning tree that visits all of the graph’s vertices in the shortest possible time while maintaining high accuracy.P2P Networks
In a peer-to-peer network, BFS can be used to locate all of the closest or neighbouring vertices. This will help you find the information you need faster.Web Crawlers
Using BFS, search engines and web crawlers can easily create multilevel indexes. The BFS implementation begins with the starting point, which is a web page, and afterwards proceeds to all of the links on that page.Network Broadcasting
The BFS algorithm guides a broadcast packet to locate and achieve all of the vertices for which it has an address.DFS and its Applications
DFS is a depth-first search strategy for solving or traversing the graphs or trees. The algorithm starts at the root vertex or a node and explores each and every branch prior to actually going backwards. When an end appears in any iteration, it uses stacks data structure to remember, get the next vertex, and start a search. DFS stands for Depth-first search. The following are some of the most important DFS applications:Graph with Weights
The DFS graph generates the shortest route and minimum spanning tree in a weighted graph.How to Spot a Cycle in the Graph
If we discover a back-edge all through DFS, the graph has a cycle. As a result, we must run DFS on the graph and double-check the back edges.Finding Your Way
We can specialise in finding a route between the two vertices using the DFS algorithm.Sorting by topology
It’s primarily used to schedule jobs based on the dependencies between groups of jobs. It is used in guidance scheduling, data serialisation, logic synthesis, and deciding the order of code tasks in computer science.Puzzle Solving with Only One Solution
By including nodes upon that existing path in the visited set, the DFS algorithm can easily be adapted to seek all solutions to a maze.Differences between BFS and DFS
| BFS | DFS |
| The term “BFS” stands for “Broadest First Search.” | DFS stands for “Depth First Search.” |
| To keep records of another location to visit, BFS employs a Queue Data structure. | To keep records of another location to visit, DFS employs a Stack Data structure. |
| When a goal is close to the Source, BFS performs better. | When a goal is far away from the source, DFS is preferable. |
| In comparison to DFS, BFS requires more memory. | In comparison to BFS, DFS requires less memory. |
| Backtracking is not required in BFS. | Backtracking is required in DFS. |
| You’ll never be stuck in an endless loop. | It’s possible that you’ll get stuck in an endless loop. |
| BFS moves more slowly than DFS. | DFS is more rapid than BFS. |
