BFS explores all nodes at the present depth before moving on to nodes at the next depth level. DFS, on the other hand, explores as far as possible along each branch before backtracking. Each is useful in different situations.

Breadth-first search (BFS) and depth-first search (DFS) are two fundamental graph traversal algorithms that are widely used in competitive programming. BFS explores a graph level by level, starting from a given node and visiting all its neighbors before moving on to the next level. It uses a queue data structure to keep track of nodes to be visited, ensuring that nodes are processed in the order they are discovered. BFS is particularly useful in situations where you need to find the shortest path in an unweighted graph, as it explores nodes in increasing order of distance from the starting node. Examples include finding the shortest path in a maze or checking for bipartiteness in a graph. DFS, in contrast, explores as deeply as possible along each branch before backtracking to explore other branches. It uses a stack data structure (or recursion) to manage the nodes to be visited. DFS is useful in problems where you need to explore all possible paths or find connected components in a graph. It's also the basis for algorithms like topological sorting and detecting cycles in directed graphs. While both BFS and DFS have similar time complexities (O(V + E), where V is the number of vertices and E is the number of edges), they differ in their approach and are suited to different types of problems. BFS tends to be better for finding the shortest paths or levels in a graph, while DFS is more suited for exhaustive exploration and problems involving recursion or backtracking.