Is the space complexity of planted clique recovery the same as that of detection?

08/28/2020
by   Jay Mardia, et al.
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We study the planted clique problem in which a clique of size k is planted in an Erdős-Rényi graph G(n, 1/2), and one is interested in either detecting or recovering this planted clique. This problem is interesting because it is widely believed to show a statistical-computational gap at clique size k=sqrtn, and has emerged as the prototypical problem with such a gap from which average-case hardness of other statistical problems can be deduced. It also displays a tight computational connection between the detection and recovery variants, unlike other problems of a similar nature. This wide investigation into the computational complexity of the planted clique problem has, however, mostly focused on its time complexity. In this work, we ask- Do the statistical-computational phenomena that make the planted clique an interesting problem also hold when we use `space efficiency' as our notion of computational efficiency? It is relatively easy to show that a positive answer to this question depends on the existence of a O(log n) space algorithm that can recover planted cliques of size k = Omega(sqrtn). Our main result comes very close to designing such an algorithm. We show that for k=Omega(sqrtn), the recovery problem can be solved in O((log*n-log*k/sqrtn) log n) bits of space. 1. If k = omega(sqrtnlog^(l)n) for any constant integer l > 0, the space usage is O(log n) bits. 2.If k = Theta(sqrtn), the space usage is O(log*n log n) bits. Our result suggests that there does exist an O(log n) space algorithm to recover cliques of size k = Omega(sqrtn), since we come very close to achieving such parameters. This provides evidence that the statistical-computational phenomena that (conjecturally) hold for planted clique time complexity also (conjecturally) hold for space complexity.

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