What are the differences between BFS and DFS implementations?

Can you explain the differences between BFS and DFS implementations?

Sure! BFS (Breadth-First Search) explores all neighbors at the present depth prior to moving on to nodes at the next depth level, while DFS (Depth-First Search) explores as far as possible along a branch before backtracking.

What data structures are typically used for BFS and DFS?

BFS typically uses a queue to keep track of the nodes to be explored, while DFS uses a stack (or recursion) to remember the path.

Can you provide a simple code example for both algorithms?

Absolutely! Here’s a simple Python example:

BFS:

from collections import deque

def bfs(graph, start):
    visited = set()
    queue = deque([start])
    while queue:
        vertex = queue.popleft()
        if vertex not in visited:
            visited.add(vertex)
            queue.extend(neighbor for neighbor in graph[vertex] if neighbor not in visited)
    return visited

DFS:

def dfs(graph, start, visited=None):
    if visited is None:
        visited = set()
    visited.add(start)
    for neighbor in graph[start]:
        if neighbor not in visited:
            dfs(graph, neighbor, visited)
    return visited

What are the time complexities of BFS and DFS?

Both BFS and DFS have a time complexity of O(V + E), where V is the number of vertices and E is the number of edges in the graph.