# BFS vs DFS

URL: https://softwaredictionary.org/compare/bfs-vs-dfs
Last updated: 2026-09-30

In short: BFS explores a graph level by level with a queue, finding shortest paths in unweighted graphs, while DFS goes deep down one branch, then backtracks.

## What is the difference between BFS and DFS?

Breadth-first search (BFS) and depth-first search (DFS) are the two basic ways to visit every node of a graph or tree. BFS visits all neighbors of the start node first, then their neighbors, spreading outward in rings. DFS picks one neighbor and keeps going deeper until it reaches a dead end, then backtracks to try the next option.

The difference comes from the data structure behind each one. BFS uses a queue, so nodes are processed in the order they were discovered, which guarantees it reaches every node by the fewest possible edges. DFS uses a stack, often the call stack through recursion, so it always continues from the most recently discovered node.

Both run in O(V + E) time, where V is the number of vertices (nodes) and E the number of edges, and both need a visited set to avoid looping forever in graphs with cycles. Many algorithms build on them: BFS powers shortest paths in unweighted graphs and friend-of-a-friend suggestions, while DFS powers cycle detection, topological sorting and maze solving.

A common misconception is that DFS finds the shortest path. It finds a path, but not necessarily the shortest one; and when edges have weights, such as road distances, neither is enough on its own, so algorithms like Dijkstra's are used instead.

| Aspect | Breadth-First Search | Depth-First Search |
| --- | --- | --- |
| Exploration order | Level by level, nearest nodes first | One branch as deep as possible, then backtrack |
| Data structure | A queue (FIFO) | A stack (LIFO) or recursion |
| Shortest path | Guaranteed in unweighted graphs | Not guaranteed |
| Memory use | Grows with the widest level of the graph | Grows with the depth of the current path |
| Time complexity | O(V + E) | O(V + E) |
| Very deep graphs | Never gets lost down one long branch | Deep recursion can overflow the call stack |
| Typical uses | Shortest paths, nearest matches, level-order traversal | Cycle detection, topological sort, puzzles and mazes |

## Choose Breadth-First Search when

- You need the shortest path in an unweighted graph.
- The target is likely close to the starting node.
- You want to process nodes level by level.

## Choose Depth-First Search when

- You need to explore every possible path, as in puzzles or backtracking.
- You are detecting cycles or ordering dependencies.
- The graph is very wide and a full level would not fit in memory.

## Frequently asked questions

**Is BFS or DFS faster?**

Both visit each node and edge once, so both take O(V + E) time. Which one finds a target sooner depends on where it is: BFS for nearby targets, DFS for deep ones.

**Which uses more memory, BFS or DFS?**

BFS usually does on wide graphs, because its queue can hold an entire level at once. DFS stores only the current path, although a very deep graph can make that path long.

**Do BFS and DFS work on trees?**

Yes. On a tree, BFS is also called level-order traversal, while DFS covers the preorder, inorder and postorder traversals.

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