  A multilayer network is composed of multiple layers, where different layers
have the same set of vertices but represent different types of interactions.
Nevertheless, some layers are interdependent or structurally similar in the
multilayer network. In this paper, we present a maximum a posteriori estimation
based model to reconstruct a specific layer in the multilayer network. The
SimHash algorithm is used to compute the similarities between various layers.
And the layers with similar structures are used to determine the parameters of
the conjugate prior. With this model, we can also predict missing links and
direct experiments for finding potential links. We test the method through two
real multilayer networks, and the results show that the maximum a posteriori
estimation is promising in reconstructing the layer of interest even with a
large number of missing links.
