First, clarify the exact variant of the graph traversal problem and the expected output, then discuss the appropriate algorithm (e.g., BFS, DFS, or topological sort) with its time and space complexity. Write clean, modular code and test it with edge cases like empty graphs, disconnected components, and cycles.
Pro tip: At Apple, interviewers value production-quality code: use meaningful variable names, handle edge cases explicitly, and discuss trade-offs between different approaches (e.g., iterative vs recursive DFS) to show depth.
Ask questions to understand the graph representation (adjacency list/matrix), whether it's directed/undirected, and the exact traversal variant (e.g., shortest path, cycle detection, topological order). Confirm input/output format and constraints.
Select the most suitable traversal algorithm (BFS for shortest path in unweighted graphs, DFS for connectivity/cycle detection, topological sort for DAGs) and justify your choice. Mention time and space complexity.
Describe the step-by-step approach, including data structures (queue, stack, visited set) and how you'll handle edge cases like disconnected components or cycles. Consider iterative vs recursive implementations.
Write clean, modular code with clear variable names and comments. Handle edge cases explicitly (e.g., empty graph, single node, self-loops). If time permits, discuss potential optimizations.
Walk through the code with a small example, then test edge cases. Discuss how you would debug and validate the solution, and mention any trade-offs or alternative approaches.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.