When should I use BFS over DFS?

Use BFS when you need to find the shortest path in an unweighted graph. BFS explores neighbors level by level, so the first time it reaches a target node, it is guaranteed to be via the shortest path. DFS is better suited for problems involving pathfinding, cycle detection, or exploring all possibilities, like in a maze.

What data structure is used to implement BFS?

BFS is typically implemented using a queue. The queue stores the nodes to be visited. When a node is visited, its neighbors are added to the back of the queue, ensuring a level-by-level traversal.

Data Structures & Algorithms

Breadth-First Search (BFS)

Master Breadth-First Search (BFS) for your coding interviews. Explore its level-order traversal, applications in shortest path problems, and practice with our AI-powered guide.

BFS Implementation

Here is a Python implementation of Breadth-First Search using a queue.


from collections import deque

def bfs(graph, start_node):
    visited = set()
    queue = deque([start_node])
    result = []

    while queue:
        node = queue.popleft()
        if node not in visited:
            visited.add(node)
            result.append(node)

            for neighbor in graph.get(node, []):
                if neighbor not in visited:
                    queue.append(neighbor)

    return result

AI Coach Tip: BFS is your go-to algorithm for finding the shortest path in an unweighted graph. Think of it as exploring a maze by checking all adjacent rooms first before moving to the next set of rooms. This level-by-level approach is key to its effectiveness in shortest-path problems.