利用朴素贝叶斯模型进行多层网络链接预测

针对多层网络链接预测中层间信息融合的问题，提出了一种利用朴素贝叶斯模型的链接预测方法。该方法结合目标层的邻域信息和辅助层相对于目标层的全局信息进行链接预测。在目标层中，根据节点对的邻域信息，利用朴素贝叶斯模型计算其连接概率；在辅助层中，计算节点对在该层有边或无边时在目标层存在链接的概率。在真实数据和合成数据上的实验结果表明：该算法在正相关和负相关的多层网络中都有很好的预测性能。

Link Prediction in Multiplex Networks Based on Naïve Bayes Model

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.