What are the practical differences between BFS and DFS?

What are the practical differences between BFS and DFS?

BFS (Breadth-First Search) explores all neighbors at the present depth before moving on to nodes at the next depth level, while DFS (Depth-First Search) goes as deep as possible down one path before backtracking. This leads to different use cases.

Can you give me examples of when to use each?

Sure! BFS is great for finding the shortest path in unweighted graphs, like in social networks. DFS is useful for tasks like topological sorting or solving puzzles where you need to explore all possibilities.

What are the time and space complexities of both?

BFS has a time complexity of O(V + E) and a space complexity of O(V), where V is vertices and E is edges. DFS also has a time complexity of O(V + E) but can have a space complexity of O(h), where h is the maximum height of the tree.

Are there any scenarios where one is preferred over the other?

Yes, BFS is preferred for shortest path problems, while DFS is often used in scenarios where you need to explore all possible solutions, like in backtracking algorithms.