To solve the problem of information fusion between layers in link predictions of multiplex networks, this paper proposes a new link prediction method based on the naïve Bayes model. The proposed method predicts links by combining the neighborhood information of target layers with the global information of distinct auxiliary layers relevant to the target layers. In a target layer, according to the neighborhood information of a node pair, the connection probability of the node pair is computed using the naïve Bayes model. In an auxiliary layer, based on whether there is a link between the node pair, the probability that the node pair has a link in the target layer is calculated. Experimental results on real and synthetic networks show that the proposed method achieves superior performance in both positively and negatively correlated multiplex networks.
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