In this blog post, we will be discussing the difference between two common algorithms for traversing graphs: BFS and DFS. We will give a brief overview of each algorithm, before discussing their pros and cons. Finally, we will provide an example to help illustrate how each algorithm works.

## What is BFS?

BFS is an algorithm for traversing or searching tree or graph data structures. It starts at the root node and explores the neighbor nodes first, before moving to the next level neighbors. BFS is often used for finding the shortest path from the start node to the goal node. BFS Breadth-First Search can be used in conjunction with another algorithm like Dijkstra’s Algorithm to find the shortest path in a weighted graph. BFS can also be used to find all nodes within one connected component. BFS is an example of a pull algorithm. Breadth-First Search was invented in 1945 by Konrad Zuse. BFS is also known as Neumann’s Algorithm and RU Neering’s Method. BFS is a blind search algorithm because it doesn’t have any information about the goal state or any heuristics to guide its search. BFS expands nodes in a layer-by-layer fashion and uses queues for storing nodes that are waiting to be explored.

## What is DFS?

DFS is an algorithm for traversing a graph. It starts at the root node and explores as far as possible along each branch before backtracking. DFS is used to find all nodes in a graph including paths to these nodes. DFS can be implemented using a stack data structure. The time complexity of DFS is O(V + E) where V is the number of vertices and E is the number of edges. DFS is typically used to find connected components in a graph. DFS can also be used to find cycles in a graph.

## Difference between BFS and DFS

BFS and DFS are two popular algorithms used for traversing a graph. BFS is a breadth-first search while DFS is a depth-first search. BFS starts from the root node and then explores all the adjacent nodes. It then moves to the next level of nodes and repeats the process until it has explored the entire graph. DFS, on the other hand, starts from the root node and explores as deep into the graph as possible before backtracking. It then moves to the next adjacent node and repeats the process. BFS is more suitable for finding shortest paths while DFS can be used for finding cycles in a graph.

## Conclusion

In conclusion, BFS is a better choice for most applications because it is more efficient. However, DFS can be useful in some rare cases. Make sure to understand the difference between these two data structures before you start coding!