Meta·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

May 2026

Summary

Meta SWE interview that came down to a graph cloning problem. Pretty focused session, they wanted to see both the implementation and the reasoning around edge cases and complexity.

Questions Asked (1)

Given a reference to a node in a connected undirected graph where each node holds a value and a list of neighbors, return a deep copy of the entire graph.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I went with BFS and kept a map from old nodes to their new clones to avoid revisiting.

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AI HintsAI Generated

Suggested Approach

Use a hash map to track visited nodes and their clones, then perform a graph traversal (DFS or BFS) starting from the given node. For each node, create a clone if not already present, and recursively clone its neighbors, linking them to the clone's neighbor list.

Pro tip: Clarify whether the graph can have cycles or duplicate edges, and mention that the hash map prevents infinite loops and ensures each node is cloned exactly once. Also, discuss trade-offs between DFS (recursive, may cause stack overflow for large graphs) and BFS (iterative, uses queue) to show depth.

1. Understand the problem and constraints

Confirm that the graph is connected, undirected, and may contain cycles. Ask about node values, edge cases (empty graph, single node), and whether the graph can be modified.

2. Choose traversal and data structures

Decide between DFS (recursive or iterative) and BFS. Use a hash map (dictionary) to map original nodes to their clones, ensuring each node is cloned once.

3. Implement the cloning logic

Start from the given node. If it's already cloned, return the clone. Otherwise, create a new node with the same value, add it to the map, then recursively clone all neighbors and append them to the clone's neighbors list.

4. Handle edge cases and verify

Test with a single node, a cycle, and a larger graph. Ensure the clone is a deep copy (no shared references) and that the original graph remains unchanged.

5. Analyze complexity and trade-offs

State time and space complexity: O(N + E) time and O(N) space for the hash map and recursion/queue. Discuss iterative vs recursive trade-offs (stack overflow risk vs code simplicity).

Key Points to Mention

Use a hash map to track visited nodes and avoid infinite loops in cyclic graphs.
Choose between DFS and BFS based on graph size and recursion depth concerns.
Ensure deep copy: new nodes with copied values and neighbor lists, not shared references.
Time complexity O(N + E) and space complexity O(N) for the hash map and traversal.
Handle edge cases: empty graph, single node, self-loops, and disconnected components (though problem says connected).
Discuss trade-offs: recursive DFS may cause stack overflow; iterative BFS uses more memory for queue.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.