What are the main applications of DFS?

DFS is widely used for pathfinding (e.g., in a maze), detecting cycles in a graph, topological sorting, and solving puzzles with a single solution, like Sudoku. Its ability to explore a branch completely before backtracking makes it ideal for these types of problems.

How can DFS be implemented?

DFS can be implemented recursively, which is often the most intuitive approach. It can also be implemented iteratively using a stack. The recursive implementation uses the call stack implicitly, while the iterative version manages the stack explicitly.

Data Structures & Algorithms

Depth-First Search (DFS)

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DFS Implementation (Recursive)

Here is a Python implementation of Depth-First Search using recursion.


def dfs(graph, start_node, visited=None):
    if visited is None:
        visited = set()
    visited.add(start_node)
    result = [start_node]

    for neighbor in graph.get(start_node, []):
        if neighbor not in visited:
            result.extend(dfs(graph, neighbor, visited))

    return result

AI Coach Tip: Think of DFS as exploring a maze by always taking the first path you see and going as deep as possible. If you hit a dead end, you backtrack and try the next available path. This 'deep dive' approach is why recursion is such a natural fit for implementing DFS.