Graph DFS: When to Use It

Start with the idea

A problem is a good match for Graph DFS when it asks for reachability, components, path existence. Here the concrete task is: Return recursive DFS order using ascending neighbors from node 0. The visible input edges=(0,1),(0,2),(1,3),(2,4) is an example, not a definition of the whole problem. Ask what operations the solution must support and what the input guarantees. Choose a representation for the operations first; when traversing, DFS explores depth and BFS follows equal-cost layers. Directed edges and disconnected components change the plan. The right choice follows from those guarantees, not from a familiar noun in the prompt.

The decisive invariant is: Explore one branch fully while marking nodes before following outgoing edges. Try to say why it applies to the task without quoting the implementation. Which fact becomes known after one update, and which possible answers remain? A graph method needs an explicit vertex-and-edge representation. For traversal, define when a vertex becomes seen: on insertion into the frontier or on removal. If that claim cannot be maintained on a legal input, the pattern is wrong or one of its preconditions is missing. A counterexample is more useful than a vague warning: change one property of the sample, trace the first two steps, and identify the exact claim that fails.

Compare costs before committing. The advertised bound is O(V+E) time · O(V) space; identify the operation responsible for it and the extra memory it needs.