Nested List Weight Sum II
A medium-tier problem at 66% community acceptance, tagged with Stack, Depth-First Search, Breadth-First Search. Reported in interviews at LinkedIn and 0 others.
Nested List Weight Sum II is a medium-difficulty problem that LinkedIn has reportedly asked. If you've done the original Nested List Weight Sum, this one flips the scoring: deeper elements count less, not more. You're given a nested list of integers and need to calculate a weight sum where depth is inverted. Acceptance sits at 65 percent, so most people who see it pass, but the inversion trick catches candidates who just copy their first-pass solution. If you hit this in a live OA and freeze on the depth reversal, StealthCoder surfaces a working solution invisibly while you stay on screen share.
Companies that ask "Nested List Weight Sum II"
Nested List Weight Sum II is the kind of problem that decides whether you pass. StealthCoder reads the problem on screen and surfaces a working solution in under 2 seconds. Invisible to screen share. The proctor sees nothing. Made by a working Amazon engineer who got tired of watching qualified friends bomb OAs they'd solve cold in an IDE.
Get StealthCoderThe core trap is assuming you need to know the max depth upfront and subtract current depth from it. That's expensive. The real pattern is a reverse pass: calculate the weight sum normally with a standard DFS or BFS, then iterate again, accumulating the previous sum and adding it to the current sum at each level. This converts the inversion into a single final calculation without pre-computing depth. Stack and Depth-First Search both work, but the two-pass or level-based BFS approach is cleaner than recursive DFS for this specific inversion. Most failures come from trying to assign weights during the first traversal instead of recognizing it's a two-phase problem. When you're live and unsure which pass order is correct, StealthCoder has the answer in seconds, keeping your momentum on the rest of the loop.
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Nested List Weight Sum II recycles across companies for a reason. It's medium-tier, and most candidates blank under the timer. StealthCoder is the hedge: an AI overlay invisible during screen share. It reads the problem and surfaces a working solution in under 2 seconds. Made by a working Amazon engineer who got tired of watching qualified friends bomb OAs they'd solve cold in an IDE. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Nested List Weight Sum II interview FAQ
Is the inversion trick hard to spot?+
Yes. Most people default to recursive DFS with depth subtraction, which works but is messier to code correctly. The two-pass or level-sum approach is cleaner and less error-prone. The inversion itself isn't algorithmically hard, just a pattern shift from the original problem.
Do I have to use DFS, or can I use BFS?+
Either works. BFS is natural for level-by-level traversal and makes the two-pass logic obvious. DFS with depth tracking also works but requires careful bookkeeping. BFS tends to be less mistake-prone for this specific variant.
How is Nested List Weight Sum II different from the original?+
The original weights elements by depth; deeper is heavier. This version inverts it: shallower elements are heavier. The algorithmic leap is recognizing you can't just subtract depth without computing max depth first. The two-pass solution avoids that entirely.
What's the time and space complexity?+
Two passes over the nested structure is O(n) time, where n is the total number of elements and nested lists. Space is O(d) for recursion stack or queue, where d is max depth. LinkedIn interviewers care less about optimizing beyond this and more that you don't re-compute depth.
Will LinkedIn ask me to handle cyclic nested lists?+
Unlikely. The problem assumes a tree structure. But if they ask follow-up edge cases, handle empty lists, single elements, and deeply nested single-branch structures mentally before they do. Most failures are the inversion pattern, not edge cases.
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