My first instinct was BFS and that was correct, but I wasted a few minutes mentally solving for shortest path length before re-reading and realizing they just wanted a boolean.
Model the problem as a graph where each word is a node and edges connect words that differ by one letter. Use BFS from the start word to find the shortest path to the end word, checking only words in the dictionary. Return true if the end word is reached, else false.
Pro tip: Optimize by using bidirectional BFS to reduce search space, and preprocess the dictionary into patterns (e.g., replacing each letter with '*') to quickly find neighbors. This demonstrates strong problem-solving skills and efficiency awareness.
Confirm edge cases: start and end words may be the same, dictionary may not contain start or end, and all words are of equal length. Discuss assumptions with the interviewer.
Treat each word as a node. Two words are connected if they differ by exactly one letter. The dictionary defines the set of valid nodes.
Use BFS to explore level by level, ensuring the shortest transformation sequence. This is optimal for unweighted graphs.
Preprocess the dictionary into a map from pattern (e.g., '*ot') to list of words. This allows O(1) neighbor lookup per pattern.
Code the BFS with visited set. Analyze time complexity: O(N * L^2) where N is dictionary size and L is word length, or O(N * L) with pattern map. Space complexity O(N).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.