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Early-stage Startup·Software Engineer·Technical Phone Screen·Junior

JuniorPrefer not to say
Apr 2026

Summary

Went through a technical interview and got asked about graph traversal, BFS and DFS stuff. Knew the material, had practiced it, but couldn't get the words out cleanly when it counted. The frustrating part is I explained it fine to myself later that same day.

Questions Asked (1)

Q1

Explain graph traversal, specifically BFS and DFS.

Algorithms & Data Structures
Author's notes

I knew this.

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AI HintsAI Generated

Suggested Approach

Start by defining graph traversal and its purpose, then clearly contrast BFS and DFS in terms of mechanics, data structures, and use cases. Use a simple example to illustrate how each works, and mention time/space complexity and when to choose one over the other.

Pro tip: Emphasize that BFS finds shortest paths in unweighted graphs, while DFS is better for topological sorting and cycle detection. Also note that both can be implemented iteratively or recursively, but recursion may cause stack overflow for large graphs.

1. Define graph traversal

Explain that graph traversal is the process of visiting all vertices in a graph systematically, and it's fundamental for searching, pathfinding, and analyzing connectivity.

2. Describe BFS

Explain that BFS explores level by level using a queue, visiting all neighbors of a node before moving deeper. Mention its use in shortest path in unweighted graphs and its O(V+E) time complexity.

3. Describe DFS

Explain that DFS explores as far as possible along each branch before backtracking, using a stack (or recursion). Mention its applications in topological sorting, cycle detection, and pathfinding, with O(V+E) time complexity.

4. Compare and contrast

Highlight key differences: BFS uses a queue, DFS uses a stack; BFS is optimal for shortest paths, DFS for exhaustive search; BFS may use more memory for wide graphs, DFS may go deep and risk stack overflow.

5. Provide examples and use cases

Give a concrete example (e.g., social network for BFS, maze solving for DFS) and mention real-world applications to reinforce understanding.

Key Points to Mention

  • BFS uses a queue (FIFO), DFS uses a stack (LIFO) or recursion
  • Time complexity: O(V + E) for both, where V is vertices and E is edges
  • Space complexity: BFS O(V) for queue, DFS O(V) for stack/recursion
  • BFS finds shortest path in unweighted graphs; DFS is used for topological sorting and cycle detection
  • Both can be used to check connectivity and traverse all nodes
  • Implementation details: visited set to avoid cycles, iterative vs recursive DFS

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.