Exact Recovery in the Latent Space Model

Ke, Chuyang, Honorio, Jean

arXiv.org Machine Learning 

We analyze the necessary and sufficient conditions for exact recovery of the symmetric Latent Space Model (LSM) with two communities. In a LSM, each node is associated with a latent vector following some probability distribution. We show that exact recovery can be achieved using a semidefinite programming approach.

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