BFS vs DFS Comparison
A detailed comparison between Breadth-First Search (BFS) and Depth-First Search (DFS) algorithms.
| Aspect | BFS (Breadth First Search) | DFS (Depth First Search) |
|---|---|---|
| Data Structure | Queue (FIFO) | Stack (LIFO) or recursion |
| Traversal Method | Level-by-level (breadth-wise) | Depth-wise (deepest path first) |
| Suitable For | Finding shortest path in unweighted graphs | Deep path exploration, backtracking, cycle detection |
| Speed | Slower | Faster |
| Memory Usage | Higher (stores many nodes) | Lower (stores path nodes) |
| Infinite Loop | No issue (if visited nodes tracked) | Possible without visited node checks |
| Application Examples | Shortest path, bipartite graph check | Topological sorting, strongly connected components, puzzle solving |
BFS Visualization
A
B
C
D
E
F
G
Order: A → B → C → D → E → F → G
BFS explores all nodes at the present depth level before moving on to nodes at the next depth level.
DFS Visualization
A
B
D
E
Order: A → B → D → E → C → F → G
DFS explores as far as possible along each branch before backtracking.
When to Use Each Algorithm
Use BFS when:
- You need to find the shortest path in an unweighted graph
- You need to traverse a tree level by level
- You need to find connected components in a graph
- You need to check if a graph is bipartite
Use DFS when:
- You need to explore all possible paths in a graph
- You need to perform topological sorting
- You need to detect cycles in a graph
- Memory is a constraint (DFS uses less memory than BFS)