BFS vs DFS: Traversal Order and Data Structure
Overview Both are algorithms for visiting every node in a tree or graph, but they differ in which node they explore next. BFS spreads outward level by level using a queue, while DFS plunges down one path as far as possible before backtracking, using a stack. Comparison Diagram BFSDFS12345671253467outinQueue (FIFO)push/popStack (LIFO) Comparison Table Aspect BFS DFS Underlying structure Queue (FIFO) Stack (FILO), often via recursion Traversal pattern Explores all neighbors at current depth before going deeper Follows one branch to its end before backtracking Order nodes are visited Level by level (breadth-first) Branch by branch (depth-first) Memory usage O(width) — can be large for wide/bushy graphs O(depth) — can be large for deep graphs Shortest path guarantee Yes, on unweighted graphs (first visit = shortest path) No, may find a longer path first Implementation style Iterative with explicit queue Recursive, or iterative with explicit stack Risk of infinite loop Low with visited-set on cyclic graphs Higher on cyclic graphs without visited-set, or infinite depth Typical use cases Shortest path, level-order processing, web crawling by hops Topological sort, cycle detection, maze/backtracking problems Key Differences BFS uses a queue and expands outward level by level, while DFS uses a stack (or recursion) and dives deep before backtracking. BFS guarantees the shortest path on unweighted graphs; DFS does not. BFS memory cost scales with graph width, while DFS memory cost scales with graph depth. DFS naturally supports backtracking algorithms like maze solving and topological sort. Both require a visited set to avoid infinite loops on cyclic graphs. When to Use Each BFS ...