Abstract：Hidden Naive Bayesian Model (HNB) and Tree Augmented Naive Bayesian Model (TAN) alleviate the strong independence assumption of Local Naive Bayesian Model (LNB) by mining the intrinsic associations between co-neighboring nodes, but ignore that there are both closely correlated nodes and relatively independent nodes in the real network. On this basis, a package criterion is designed, which divides the co-neighboring nodes into correlated co-neighboring nodes and independent co-neighboring nodes according to the degree of association. Then, packaging HNB and TAN respectively, so that the packaged-based hybrid naive Bayesian models are obtained. On FWFW networks with high average number of co-neighbors, the AUC values of the HNB and TAN models after packaging are increased by 12% and 11.6%, respectively. The experimental results show that the proposed method can effectively improve the link prediction performance and has good robustness.
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