Start by clarifying requirements and edge cases, then outline a parser that normalizes various input formats into a directed graph. Describe topological sort using Kahn's algorithm, cycle detection with DFS, and strategies for malformed/duplicate inputs. Finally, analyze complexity and discuss adaptations for streaming and large graphs.
Pro tip: Emphasize that you would validate and sanitize input early, and that you'd use iterative algorithms to avoid stack overflow on large graphs. Also, mention that for streaming, you'd need to handle incremental updates and possibly use a dynamic topological sort algorithm.
Ask about input formats, expected outputs, and how to handle malformed lines, duplicates, and cycles. Confirm whether the graph is directed and acyclic by nature or if cycles are possible.
Propose a flexible parser that recognizes patterns like 'install A after B' and 'C->A', extracts nodes and edges, and builds an adjacency list. Handle malformed lines by logging and skipping, and deduplicate edges.
Use Kahn's algorithm for topological ordering; if a cycle exists, detect it using DFS and return a minimal cycle. Explain how to extract a minimal cycle from the DFS back edge.
State that both are O(V+E) for parsing, topological sort, and cycle detection. Discuss space usage for adjacency list and auxiliary data structures.
For streaming, propose incremental parsing and dynamic topological sort; for large graphs, suggest external memory algorithms, parallel processing, or approximate methods if exact ordering is not required.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.