# Difference between BFS and DFS

**Breadth First Search:****BFS **stands for **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. In BFS, one vertex is selected at a time when it is visited and marked then its adjacent are visited and stored in the queue. It is slower than DFS. **Example**:

Input:A / \ B C / / \ D E F

**Output:**

A, B, C, D, E, F

**Depth First Search:****DFS **stands for **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. **Example:**

Input:A / \ B C / / \ D E F

**Output:**

A, B, D, C, E, F

__BFS vs DFS__

S.No | BFS | DFS |
---|---|---|

1. | BFS stands for Breadth First Search. | DFS stands for Depth First Search. |

2. | BFS(Breadth First Search) uses Queue data structure for finding the shortest path. | DFS(Depth First Search) uses Stack data structure. |

3. | BFS can be used to find single source shortest path in an unweighted graph, because in BFS, we reach a vertex with minimum number of edges from a source vertex. | In DFS, we might traverse through more edges to reach a destination vertex from a source. |

4. | BFS is more suitable for searching vertices which are closer to the given source. | DFS is more suitable when there are solutions away from source. |

5. | 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, then explore all paths through this decision. And if this decision leads to win situation, we stop. |

6. | 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. |

7. | Here, siblings are visited before the children | Here, children are visited before the siblings |

8. | In BFS there is no concept of backtracking. | DFS algorithm is a recursive algorithm that uses the idea of backtracking |

9. | BFS is used in various application such as bipartite graph, and shortest path etc. | DFS is used in various application such as acyclic graph and topological order etc. |

10. | BFS requires more memory. | DFS requires less memory. |

Please also see BFS vs DFS for Binary Tree for the differences for a Binary Tree Traversal.