Maximum Total Importance of Roads
A medium-tier problem at 69% community acceptance, tagged with Greedy, Graph, Sorting. Reported in interviews at Hudson River Trading and 0 others.
Maximum Total Importance of Roads is a medium-difficulty greedy problem that Hudson River Trading asks during their online assessments. You're given a graph where each node has an importance value, and you need to maximize the total importance contribution across a set of roads (edges). The 69% acceptance rate is deceptive, it masks a conceptual gap most candidates hit: the greedy choice isn't obvious until you see it. If this problem lands in your live OA and you freeze on the strategy, StealthCoder solves it invisibly in seconds, surfacing the working solution before you lose time to trial-and-error.
Companies that ask "Maximum Total Importance of Roads"
Maximum Total Importance of Roads 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 FAANG engineer who treats the OA the way companies treat hiring: as a game with rules you should know.
Get StealthCoderThe trick is recognizing that each node's contribution to total importance is proportional to its degree (how many roads it's connected to). You sort nodes by importance descending, then greedily connect each node to as many previous nodes as possible. Most candidates miss this and either try dynamic programming or attempt brute-force enumeration of valid graphs. The greedy approach works because you want high-importance nodes to have maximum degree. Once you see the pattern, the implementation is straightforward: sort, iterate, connect. StealthCoder is your safety net if you hit the assessment without internalizing this specific greedy insight, it reads the problem, identifies the pattern, and delivers a working solution while the proctor sees nothing.
Pattern tags
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Maximum Total Importance of Roads 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 FAANG engineer who treats the OA the way companies treat hiring: as a game with rules you should know. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Maximum Total Importance of Roads interview FAQ
Is this problem actually asked at Hudson River Trading?+
Yes, it appears in their online assessment pipeline. Hudson River Trading is a quantitative trading firm that values algorithmic thinking, so greedy graph optimization problems like this one are well within their typical OA difficulty range.
Why is the acceptance rate 69% if it's a medium problem?+
The acceptance rate reflects that candidates who see the greedy pattern solve it quickly, but those who don't often time out or submit incorrect solutions. The gap between seeing the trick and not is sharp. Many candidates waste time exploring wrong directions before landing on the correct greedy strategy.
How does Sorting interact with this problem?+
Sorting by importance (descending) is the foundation of the greedy algorithm. You process high-importance nodes first to ensure they accumulate maximum degree. Without sorting, the greedy choice becomes arbitrary and doesn't yield the optimal result.
Should I use a Heap for this, or is Sorting enough?+
Pure sorting is sufficient for the greedy approach. A heap could optimize dynamic updates if the problem had constraints like adding/removing nodes, but for the static version, sort once and iterate. Don't over-engineer it.
What's the most common mistake candidates make on this problem?+
Overthinking the graph structure. Candidates often try to construct a specific tree or cycle shape, or they attempt DP on subsets. The greedy insight, connect greedily by importance, is deceptively simple once you see it, but invisible before that.
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