Use a two-pointer technique to traverse the word and abbreviation simultaneously, parsing digits as skip counts and comparing letters directly. Validate edge cases such as leading zeros and empty digit sequences during parsing.
Pro tip: Clarify whether the abbreviation can contain digits that are part of the word (e.g., word 'a1' and abbr 'a1' where '1' is a digit) and handle them as skip counts, not literal characters. Also, discuss time and space complexity upfront to show efficiency awareness.
Confirm with the interviewer that digits represent skipped characters, no leading zeros, and digit sequences cannot be empty. Discuss cases like empty word, empty abbreviation, and consecutive digits.
Set pointers i for word and j for abbreviation, both starting at 0. Iterate while both pointers are within bounds.
If abbr[j] is a digit, parse the full number, checking for leading zeros and ensuring the number is positive. Then advance i by that number and j past the digits.
If abbr[j] is a letter, compare it with word[i]. If they match, increment both pointers; otherwise, return false.
After the loop, ensure both pointers have reached the end of their respective strings. If not, the abbreviation is invalid.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The hashmap from original node to its clone is the whole problem.
Use a graph traversal (DFS or BFS) to visit each node exactly once, maintaining a hash map from original nodes to their copies. For each visited node, create its copy if not already present, then recursively or iteratively clone all its neighbors and connect them to the copy.
Pro tip: Clarify whether the graph can have cycles or self-loops, and mention that the hash map prevents infinite loops and ensures each node is copied only once. Also, discuss the trade-off between DFS (recursive, may hit stack limits) and BFS (iterative, uses queue) based on graph size.
Confirm that the graph is connected, undirected, and may contain cycles. Ask about constraints like node count, whether nodes have unique values, and if the graph can be empty (null input).
Decide between DFS (recursive or iterative) and BFS. Use a hash map (dictionary) to map original nodes to their cloned counterparts, ensuring each node is copied once.
During traversal, for each original node, create a copy if not already in the map. Then for each neighbor, recursively clone it (if needed) and add the cloned neighbor to the copy's neighbor list.
If the input node is null, return null. After traversal, return the clone of the starting node from the hash map.
State that time and space complexity are O(N + E) where N is number of nodes and E is number of edges. Walk through a simple example (e.g., two nodes connected) to verify correctness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.