My first instinct was DFS and I just went with it.
Model the problem as a graph traversal where groups and users are nodes, and edges represent membership (group-to-group or group-to-user) and ownership (user-to-device). Use DFS or BFS with a visited set to handle cycles and avoid redundant work, collecting device IDs from users encountered. Return the unique set of device IDs.
Pro tip: Mention that you would deduplicate devices and groups early to avoid exponential blowup, and discuss trade-offs between iterative and recursive implementations for production systems with deep hierarchies.
Confirm the structure: groups contain users and groups, users own devices. Ask about scale, cycle frequency, and whether device IDs are globally unique. Clarify if the starting set can include users directly.
Select DFS or BFS with a visited set for groups (and optionally users) to handle cycles. Use a set for device IDs to ensure uniqueness. Consider adjacency lists for efficient lookup.
Starting from each group in the input set, recursively expand: for each group, iterate its members; if a member is a user, add their devices; if a group, recurse if not visited. Mark groups as visited to prevent cycles.
Address empty starting set, groups with no users, and deep recursion (use iterative stack to avoid stack overflow). Discuss memoization if the same groups are queried repeatedly.
State time complexity O(G + U + D) where G, U, D are reachable groups, users, devices; space O(G + D) for visited and result sets. Mention potential extensions like permission checks or caching.
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