Run a profiler first – see where the time is really spent. Then step back and think about the algorithm’s theoretical complexity. With a DFS that becomes quadratic you’re usually recomputing the same sub‑problem over and over. Spot that by looking for repeated calls with the same arguments or nested loops inside the recursion. Add memoization, or better yet rewrite the routine iteratively so you keep a stack of nodes instead of calling yourself again. Also watch for needless copies of large structures in each call – that can drive the cost up too. After you tweak it, re‑profile with a bigger dataset to make sure the hotspot is gone. That’s the systematic way to hunt down those hidden bottlenecks.