Semidefinite Programs for Exact Recovery of a Hidden Community


Bruce Hajek, Yihong Wu, Jiaming Xu ;
29th Annual Conference on Learning Theory, PMLR 49:1051-1095, 2016.


We study a semidefinite programming (SDP) relaxation of the maximum likelihood estimation for exactly recovering a hidden community of cardinality K from an n \times n symmetric data matrix A, where for distinct indices i,j, A_ij ∼P if i, j are both in the community and A_ij ∼Q otherwise, for two known probability distributions P and Q. We identify a sufficient condition and a necessary condition for the success of SDP for the general model. For both the Bernoulli case (P=\rm Bern(p) and Q=\rm Bern(q) with p>q) and the Gaussian case (P=\mathcalN(μ,1) and Q=\mathcalN(0,1) with μ>0), which correspond to the problem of planted dense subgraph recovery and submatrix localization respectively, the general results lead to the following findings: (1) If K=ω( n /\log n), SDP attains the information-theoretic recovery limits with sharp constants; (2) If K=Θ(n/\log n), SDP is order-wise optimal, but strictly suboptimal by a constant factor; (3) If K=o(n/\log n) and K \to ∞, SDP is order-wise suboptimal. The same critical scaling for K is found to hold, up to constant factors, for the performance of SDP on the stochastic block model of n vertices partitioned into multiple communities of equal size K. A key ingredient in the proof of the necessary condition is a construction of a primal feasible solution based on random perturbation of the true cluster matrix.

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