隐朴素贝叶斯模型(HNB)和树增强朴素贝叶斯模型(TAN)通过挖掘共邻节点之间的内在关联缓解局部朴素贝叶斯模型(LNB)的强独立性假设,却忽略了真实网络中同时存在关联紧密的节点和相对独立的节点。在此基础上设计一种分包准则,将共邻节点划分为关联共邻节点和独立共邻节点,然后分别对HNB和TAN做分包改进,提出基于分包的混合朴素贝叶斯模型。在平均共邻节点数高的FWFW网络上,分包后HNB和TAN模型与原模型相比AUC值分别提升12%和11.6%。实验结果表明,所提方法能有效提升链路预测性能,并且具有良好的鲁棒性。
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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