# Difference Between BFS and DFS

Both DFS and BFS are traversal algorithms for the given graphs. Now, before we look into the basic difference between BFS and DFS, let us know in detail about DFS and BFS separately.

## What is BFS?

Breadth First Search or BFS is a vertex-based algorithm used for finding the shortest path in a graph. The BFS technique traverses any graph in the breadth-ward motion. It then uses a queue for remembering that the next vertex must begin the search after reaching a dead end in any iteration. It basically selects one vertex at a time (by visiting and marking it). Then it visits the adjacent one to store it in the queue. Thus, BFS uses a Queue Data Structure that works on FIFO (First In, First Out).

## What is DFS?

Depth First Search or DFS is an edge-based algorithm. It traverses any graph in a depth-ward motion. It then uses a stack for remembering that the next vertex must begin the search after reaching a dead end in any iteration. It performs its technique in two stages. In the first stage, it pushes the first vertices into the stack. In the second stage, if there are no vertices, then it pops the visited vertices. BFS basically uses a Stack Data Structure.

## Difference Between BFS and DFS

 Parameter BFS DFS Definition It is an abbreviation for Breadth First Search. It is an abbreviation for Depth First Search. Structure of Data It uses the Queue Data Structure for finding the shortest path. It uses the Stack Data Structure for finding the shortest path. Speed It is comparatively slower than the DFS method. It is comparatively faster than the BFS method. Distance from Source BFS acts better when the target stays closer to the source. DFS acts better when the target stays farther from the source. Suitability for Decision Tree Since BFS considers all of its neighbors, it is not very suitable for the decision trees used in puzzle games. DFS is comparatively much more suitable for the decision trees. Within a decision, it allows a user to traverse further to augment that decision. If you reach a conclusion, you reach the win situation, and you stop. Type of List BFS operates using the FIFO list. DFS operates using the LIFO list. Tracking Method BFS uses the queue to keep track of the next location that it should visit. DFS uses the stack to keep track of the next location that it should visit. Type of Solution It ensures a shallow path solution. BFS directly finds the shortest path that leads to a solution. It does not ensure a shallow path solution. DFS first heads to the bottom of any subtree. Then, it backtracks. Tree Path It traverses a path according to the tree level. It traverses a path according to the tree depth. Backtracking You don’t need to backtrack in BFS. You need to follow a backtrack in DFS. Loop Trapping A user can happen to get trapped in a finite loop. Any user can possibly get trapped in an infinite loop. In Case of No Goal If a user doesn’t find a goal, they may have to expand various nodes before they find the solution. If a user does not find any goal, it may lead to the leaf node backtracking. Reaching Destination Vertex You can use BFS to find a single source of the shortest path in the case of an unweighted graph. It is because, in BFS, one can easily reach the vertex with a minimum number of edges from the source vertex. You might need to traverse through more edges to find and reach the destination vertex from your source. Suitable Vertices BFS works better when a user searches for the vertices that stay closer to any given source. DFS works better when a user can find the solutions away from any given source. Memory The amount of memory required for BFS is more than that of DFS. The amount of memory required for DFS is less than that of BFS. Complexity of Time The time complexity of BFS is O (V+E) when a user deploys the Adjacency List and O(V^2) when the user deploys Adjacency Matrix. Here, E refers to the edges, and V refers to the vertices. The time complexity of BFS is also equivalent to O (V+E) when a user deploys the Adjacency List and O(V^2) when the user deploys Adjacency Matrix, in which E refers to the edges and V refers to the vertices.

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