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Bytedance·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
May 2026

Summary

Bytedance SWE interview with a graph algorithm question. Pretty focused session, just the one problem but they wanted a clean implementation.

Questions Asked (1)

Q1

Given a DAG, print the execution order of its nodes using topological sort.

Algorithms & Data Structures
Author's notes

I went with Kahn's algorithm (BFS-based with in-degree tracking) because I always fumble the DFS post-order version under pressure.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem: confirm the graph is a DAG and discuss whether to return any valid topological order or a specific one. Then present both Kahn's algorithm (BFS-based) and DFS-based topological sort, explaining their trade-offs, and walk through a small example to demonstrate correctness.

Pro tip: Mention that Kahn's algorithm can also detect cycles (if the result size is less than the number of nodes), which is a common follow-up. Also, discuss how to handle large graphs by using iterative DFS to avoid stack overflow.

1. Clarify requirements and edge cases

Confirm that the graph is a DAG, ask about input format (adjacency list or matrix), and whether to return any valid order or a specific one. Discuss edge cases like empty graph, single node, and disconnected components.

2. Choose an algorithm and explain it

Select either Kahn's algorithm (BFS with in-degree tracking) or DFS-based topological sort. Explain the core idea: for Kahn's, repeatedly remove nodes with in-degree 0; for DFS, perform post-order traversal and reverse the result.

3. Walk through a small example

Trace the algorithm on a simple DAG (e.g., 5 nodes) to demonstrate how the order is produced. Highlight how in-degrees are updated or how DFS explores and backtracks.

4. Analyze complexity and trade-offs

State time and space complexity: O(V+E) for both approaches. Compare them: Kahn's is iterative and detects cycles easily; DFS is simpler but may cause stack overflow for large graphs.

5. Discuss extensions and optimizations

Mention cycle detection, lexicographically smallest topological order (using a min-heap), and handling large graphs with iterative DFS or parallel processing.

Key Points to Mention

  • Definition of topological sort and its application in scheduling, dependency resolution, etc.
  • Kahn's algorithm: using a queue and in-degree array, time complexity O(V+E).
  • DFS-based approach: post-order traversal and reversing the result, time complexity O(V+E).
  • Cycle detection: if the topological order doesn't include all nodes, the graph has a cycle.
  • Handling disconnected graphs: ensure all components are processed.
  • Space complexity: O(V+E) for storing the graph and auxiliary data structures.

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