All Ancestors of a Node in a Directed Acyclic Graph
A medium-tier problem at 62% community acceptance, tagged with Depth-First Search, Breadth-First Search, Graph. Reported in interviews at Palantir Technologies and 0 others.
You're hunting ancestors in a DAG, and Palantir has asked it. The problem looks deceptively simple: find all ancestors for each node. But the obvious approach of running DFS from every single node will time out or TLE on large graphs. The trick isn't the algorithm itself, it's recognizing when to flip the direction of traversal and when to cache aggressively. If you blank on this during the live OA, StealthCoder surfaces a working solution in seconds, invisible to the proctor.
Companies that ask "All Ancestors of a Node in a Directed Acyclic Graph"
All Ancestors of a Node in a Directed Acyclic Graph is the kind of problem that decides whether you pass. StealthCoder reads the problem on screen and surfaces a working solution in under 2 seconds. Invisible to screen share. The proctor sees nothing. Made by an Amazon engineer who watched the leaked-problem repo become an industry secret. He decided you should have it too.
Get StealthCoderMost candidates default to DFS from each target node backward through incoming edges. That works on small inputs but collapses at scale. The real pattern: reverse the graph, then run DFS or BFS from each node forward, collecting all reachable nodes. This flips the problem geometry and unlocks memoization. Topological sort doesn't solve it directly, but understanding node ordering and traversal dependency helps you recognize which nodes can be pruned early. The gotcha is implementation detail: building the adjacency list correctly in the reversed direction, and tracking visited state per-query versus globally. StealthCoder is your hedge if you've drilled topological sort but not yet seen how ancestor queries flip the graph traversal pattern.
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All Ancestors of a Node in a Directed Acyclic Graph recycles across companies for a reason. It's medium-tier, and most candidates blank under the timer. StealthCoder is the hedge: an AI overlay invisible during screen share. It reads the problem and surfaces a working solution in under 2 seconds. Made by an Amazon engineer who watched the leaked-problem repo become an industry secret. He decided you should have it too. Works on HackerRank, CodeSignal, CoderPad, and Karat.
All Ancestors of a Node in a Directed Acyclic Graph interview FAQ
Is this just a straightforward DFS backward from each node?+
Not at scale. Naive backward DFS from each node recomputes overlapping subproblems repeatedly. The optimization is to reverse the graph and traverse forward, then cache results per starting node. This cuts redundant work and passes larger test cases.
Why is topological sort listed as a topic if the problem is just finding reachability?+
Topological sort isn't the direct solution but teaches graph ordering discipline. Understanding node dependencies and traversal order helps you reason about which ancestors must be visited before others, and prunes unnecessary branches during BFS or DFS.
Will BFS or DFS work, or does one dominate?+
Both work. BFS is often simpler to code correctly and scales well if you memoize visited nodes. DFS is more compact but risk stack overflow on very deep DAGs. Pick BFS if you're under time pressure in the OA.
How do I know if my solution is efficient enough?+
If you're running DFS from scratch for every node without reversing the graph or caching, you'll likely TLE. Reversed graph plus memoization should handle medium-size DAGs (thousands of nodes and edges) comfortably.
Does acceptance rate of 62% mean this is easy or hard?+
62% is solid but not trivial. Most TLEs come from the naive approach. Once you see the graph-reversal pattern, implementation is straightforward. It's a problem that separates preparation from improvisation.
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