Start by clarifying the problem and constraints, then discuss possible graph representations and algorithms, focusing on time and space complexity. Choose the most efficient approach, explain your reasoning, and walk through a small example before coding.
Pro tip: Demonstrate structured problem-solving by explicitly stating assumptions and trade-offs, and always analyze complexity before writing code. This shows maturity and prevents miscommunication.
Ask questions to understand input/output, constraints, edge cases, and whether the graph is directed/weighted/cyclic.
Brainstorm multiple algorithms (e.g., BFS, DFS, Dijkstra, topological sort) and compare their time/space complexity.
Choose the optimal approach based on constraints, explain why it's best, and outline the algorithm steps.
Trace the algorithm on a small example to verify correctness and catch edge cases.
Write clean code, then test with normal, edge, and large cases, discussing potential optimizations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.