Differences between BFS and DFS - Mumbai

Traversal Order:

• BFS: Visits nodes level by level, exploring all the neighbors of a node before moving on to the next level.

• DFS: Explores as far as possible along one branch before backtracking.

Data Structure:

• BFS: Typically implemented using a queue data structure, where nodes are added to the back of the queue and removed from the front.

• DFS: Typically implemented using a stack data structure or recursion. The recursive nature of DFS naturally uses the call stack.

Memory Usage:

• BFS: Requires more memory as it needs to keep track of all the nodes at the current level.

• DFS: Generally uses less memory, especially in a well-optimized recursive implementation.

Completeness:

• BFS: Guarantees the shortest path in an unweighted graph.

• DFS: Does not necessarily find the shortest path and might explore long paths before shorter ones.

Applications:

• BFS: Used in finding the shortest path, puzzle solving, network flow problems.

• DFS: Used in topological sorting, maze solving, finding connected components.

Performance:

• BFS: Can be slower in practice on large graphs due to memory requirements and the need to maintain a queue.

• DFS: Can be more efficient in terms of memory but might take longer to find a solution.

Search Order:

• BFS: Expands the shallowest node first.

• DFS: Expands the deepest unexplored node first.

Implementation: