Usually when I've had to walk a graph, I've always used depth-first search because of the lower space complexity. I've honestly never seen a situation that calls for a breadth-first search, although my experience is pretty limited.
When does it make sense to use a breadth-first search?
UPDATE: I suppose my answer here shows a situation where I've used a BFS (because I thought was a DFS). I'm still curious to know though, why it was useful in this case.
When you want to reach a node by traversing as few edges as possible, i.e. when you want to find the shortest path in an unweighted graph.
Also the space complexity of a depth first search can be higher than that of a breadth first search when e.g. each node has only one child node, i.e. when the graph is deep but not very broad.
Could be used for solving a search problem with minimum number of steps. Going in depth first could lead (if not limited somehow) to infinite depth.
Ex: finding the closest node to the root that satisfies a condition.
When you need to get the shortest path to a vertex from a graph with no edge weight.
In depth first search you may find "local" solutions - to truly find a global solution you need to traverse the entire graph (or use a heuristic).
BFS is sometimes really useful. Suppose you have a tree that represents let's say WBS. You may want to present to your user only 1 level of it.
If your search domain is infinite, depth-first-search doesn't guarantee to terminate/find a solution, even if a finite solution does exist.
Also you can see more elaborate algorithms like A* to be a special subtype of breadth-first-search.
In general, bfs is both optimal and complete (with finite branching factor) while dfs isn't.
When does it make sense to use a breadth-first search?
For example, when you need to find the shortest path in a graph - DFS just can't do that. There are some other applications, but, in general, DFS and BFS are the same algorithm working on different data structures (BFS uses queue and DFS works with stack).
calculate complexity of print shortest path function based on breadth first search
explain how to find the minimum key stored in a b-treeand how to find the predcessor of a given key stored in b-tree .