How do different traversal algorithms compare in terms of efficiency?

What are the main types of traversal algorithms used in data structures?

The main types of traversal algorithms are Depth-First Search (DFS) and Breadth-First Search (BFS). DFS explores as far as possible along a branch before backtracking, while BFS explores all neighbors at the present depth prior to moving on to nodes at the next depth level.

How do their efficiencies compare in terms of time and space complexity?

DFS has a time complexity of O(V + E) and a space complexity of O(h), where h is the maximum height of the tree. BFS also has a time complexity of O(V + E) but a space complexity of O(V) due to the need to store all nodes at the current level.

In what scenarios would you prefer one over the other?

You might prefer DFS when you need to explore all paths and find solutions quickly, especially in puzzles. BFS is better for finding the shortest path in unweighted graphs.

Can you give an example of a real-world application for each?

Sure! DFS is often used in maze solving and topological sorting, while BFS is commonly used in social networking applications to find the shortest connection path.