cycle detection

Graph traversal sits beneath a large family of interview problems. What matters is knowing the BFS-vs-DFS tradeoff (shortest path vs memory shape) and applying it cleanly, then getting the directed-vs-undirected cycle gotcha that trips up most candidates.

Topological sort arranges a DAG so dependencies come first, the backbone of build systems, schedulers, and ML/data pipeline DAGs. Interviewers look for Kahn's algorithm (or DFS) plus cycle detection. The answer follows.

Course Schedule collapses to a single question: is the prerequisite graph a DAG? The signal is seeing the graph framing and applying topological sort or DFS to detect a cycle. Here is the answer.

Sierra's signature build screen: cells hold values or formulas that reference other cells, and editing one has to recompute its dependents without infinite loops. The signal is the dependency graph plus cycle detection. Here is the topological-eval design.

An org-chart recursion walks a tree, but a general graph has multiple paths and back-edges. The signal is building up the visited path to prune cycles and ranking paths by cost to land the shortest one.