【摘要】 为了解决基于局部信息的链路预测算法忽略了节点邻居信息对节点相似性度量影响的问题，提出了一种基于相对熵和节点局部结构的链路预测算法.首先，采用二阶本地网络描述节点的局部结构；然后，通过相对熵的重新定义刻画了节点之间的结构相似性；最后，利用相对熵来度量节点的结构相似性，考虑节点邻居的结构信息，提出相对熵度量节点结构相似性指标.在7个实际网络数据集上的仿真实验测试表明：相比其他基于局部和全局信息的相似性指标，所提方法在曲线下面积衡量标准下能够取得更好的效果，并且适用于平均聚集系数小的网络，在大规模网络上也有较好的表现.更多还原

【Abstract】 In order to solve the problem that the link prediction method based on local information ignores the influence of neighbor structure information on the similarity measurement of nodes, a link prediction method based on relative entropy and local structure of nodes is proposed.Firstly, the secondorder local network is introduced to describe the local structure of nodes; then, the structural similarity between nodes is described by redefining the relative entropy; finally, the structural similarity of nodes is measured based on relative entropy, and the structural similarity index of the node structure is proposed considering the structure information of the neighbor.Simulation experiments on seven actual network data sets show that compared with other similarity indicators based on local and global information, the proposed method can achieve better results within the area under curve measurement standard, and is suitable for networks with small average aggregation coefficient, which has better performance in large-scale networks.更多还原