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This approach uses sorting to calculate the h-index. The idea is to sort the array of citations in descending order. Then, find the maximum number h such that there are h papers with at least h citations. This can be efficiently determined by iterating over the sorted array.
Time Complexity: O(n log n) due to sorting, Space Complexity: O(1) since the sorting is in place.
1def hIndex(citations):
2 citations.sort(reverse=True)
3 for i, c in enumerate(citations):
4 if c < i + 1:
5 return i
6 return len(citations)
7
8citations = [3, 0, 6, 1, 5]
9print("H-Index:", hIndex(citations))The Python solution sorts the array in descending order using Python's built-in sort method. It then iterates over the sorted array to find the appropriate h-index value.
Given the constraints where citation counts do not exceed 1000 and the number of papers is at most 5000, a counting sort or bucket sort can be used. This approach involves creating a frequency array to count citations. Then traverse the frequency array to compute the h-index efficiently.
Time Complexity: O(n + m) where n is citationsSize and m is the maximum citation value, Space Complexity: O(m).
1#
This C implementation uses a frequency array to count papers for citation values. It accumulates from the back (high values) to find the point where the count matches or exceeds the index.