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My first instinct was DFS and I started going down that path before catching myself.
Start by clarifying the problem constraints and edge cases, then propose a BFS-based solution that finds the shortest cycle by exploring each vertex as a potential cycle start. Explain how to avoid counting the same edge back and forth, and analyze the time complexity.
Pro tip: Mention that for unweighted graphs, BFS from each vertex gives the shortest cycle in O(n*(n+m)) time, but you can optimize by only considering vertices with degree ≥ 2 and stopping early if a cycle of length 3 is found.
Ask about graph properties (connected? simple? self-loops? multi-edges?) and confirm return value for no cycle. Discuss constraints on n and m to guide algorithm choice.
Propose BFS from each vertex to find shortest cycle, explaining why BFS works for unweighted graphs. Alternatively, mention DFS with parent tracking but note BFS is simpler for shortest path.
For each vertex s, run BFS, tracking parent to avoid immediate backtracking. When encountering a visited vertex not parent, a cycle is found; compute its length and update minimum.
State time complexity O(n*(n+m)) and space O(n). Mention optimizations: skip vertices with degree < 2, stop if cycle length 3 found, and consider only vertices with smallest degree.
Walk through a simple graph (e.g., triangle) and a graph with no cycle to verify correctness. Discuss handling of disconnected graphs.
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