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

Layer reconstruction and missing link prediction of multilayer network with a Maximum A Posteriori estimation