BFS i.e. Breadth-First Search, is a vertex-based technique for finding the shortest path in the graph. It uses a Queue data structure that follows first in first out. DFS i.e. Depth First Search, is an edge-based technique. It uses the Stack data structure and performs two stages, first visited vertices are pushed into the stack, and second if there are no vertices then visited vertices are popped.
| Parameters | BFS | DFS |
|---|---|---|
| Stands for | BFS stands for Breadth First Search. | DFS stands for Depth First Search. |
| Data Structure | BFS(Breadth First Search) uses Queue data structure for finding the shortest path. | DFS(Depth First Search) uses Stack data structure. |
| Definition | BFS is a traversal approach in which we first walk through all nodes on the same level before moving on to the next level. | DFS is also a traversal approach in which the traverse begins at the root node and proceeds through the nodes as far as possible until we reach the node with no unvisited nearby nodes. |
| Technique | BFS can be used to find a single source shortest path in an unweighted graph because, in BFS, we reach a vertex with a minimum number of edges from a source vertex. | In DFS, we might traverse through more edges to reach a destination vertex from a source. |
| Conceptual Difference | BFS builds the tree level by level. | DFS builds the tree sub-tree by sub-tree. |
| Approach used | It works on the concept of FIFO (First In First Out). | It works on the concept of LIFO (Last In First Out). |
| Suitable for | BFS is more suitable for searching vertices closer to the given source. | DFS is more suitable when there are solutions away from source. |
| Suitable for Decision Trees their winning | BFS considers all neighbors first and therefore not suitable for decision-making trees used in games or puzzles. | DFS is more suitable for game or puzzle problems. We make a decision, and the then explore all paths through this decision. And if this decision leads to win situation, we stop. |
| Time Complexity | The Time complexity of BFS is O(V + E) when Adjacency List is used and O(V^2) when Adjacency Matrix is used, where V stands for vertices and E stands for edges. | The Time complexity of DFS is also O(V + E) when Adjacency List is used and O(V^2) when Adjacency Matrix is used, where V stands for vertices and E stands for edges. |
| Visiting of Siblings/ Children | Here, siblings are visited before the children. | Here, children are visited before the siblings. |
| Removal of Traversed Nodes | Nodes that are traversed several times are deleted from the queue. | The visited nodes are added to the stack and then removed when there are no more nodes to visit. |
| Backtracking | In BFS there is no concept of backtracking. | DFS algorithm is a recursive algorithm that uses the idea of backtracking |
| Applications | BFS is used in various applications such as bipartite graphs, shortest paths, etc. | DFS is used in various applications such as acyclic graphs and topological order etc. |
| Memory | BFS requires more memory. | DFS requires less memory. |
| Optimality | BFS is optimal for finding the shortest path. | DFS is not optimal for finding the shortest path. |
| Space complexity | In BFS, the space complexity is more critical as compared to time complexity. | DFS has lesser space complexity because at a time it needs to store only a single path from the root to the leaf node. |
| Speed | BFS is slow as compared to DFS. | DFS is fast as compared to BFS. |