BFS is the ideal choice for problems involving unweighted shortest paths, finding the minimum number of hops, or simulating layer-by-layer processes.
- Use BFS whenever you need to find the shortest path in an unweighted graph. Its level-by-level exploration guarantees that the first time you reach a node, it is via a shortest path.
- It's perfect for modeling processes that spread out in rounds, like rumor diffusion in a social network or the ripple effect of a broadcast.
- However, be mindful of memory. For very wide graphs where one node connects to many others, the BFS queue can become very large, potentially consuming a lot of memory.
Ideal Scenarios for BFS
| Scenario | Why BFS? |
|---|---|
| Minimum hops in a network | Levels directly correspond to hop counts |
| Closest reachable goal | The first time a goal is dequeued gives a shortest path |
| Layered simulations | Provides a natural round-based expansion |