Abstract: Enhancing the accuracy of link prediction is one of the fundamental problems in the research of complex networks. The existing node similarity-based prediction indexes do not make full use of the importance influences of the nodes in the network. In order to solve the above problem, a link prediction algorithm based on the node importance was proposed. The node degree centrality, closeness centrality and betweenness centrality were used on the basis of similarity indexes such as Common Neighbor (CN), Adamic-Adar (AA) and Resource Allocation (RA) of local similarity-based link prediction algorithm. The link prediction indexes of CN, AA and RA with considering the importance of nodes were proposed to calculate the node similarity. The simulation experiments were taken on four real-world networks and Area Under the receiver operation characteristic Curve (AUC) was adopted as the standard index of link prediction accuracy. The experimental results show that the link prediction accuracies of the proposed algorithm on four data sets are higher than those of the other comparison algorithms, like Common Neighbor (CN) and so on. The proposed algorithm outperforms traditional link prediction algorithm and produces more accurate prediction on the complex network.

Key words: complex network, centrality, similarity, link prediction, Common Neighbor (CN)

摘要： 提升链路预测精度是复杂网络研究的基础问题之一，现有的基于节点相似的链路预测指标没有充分利用网络节点的重要性，即节点在网络中的影响力。针对以上问题提出基于节点重要性的链路预测算法。该算法在基于局部相似性链路预测算法的共同邻居（CN）、Adamic-Adar（AA）、Resource Allocation（RA）相似性指标的基础上，充分利用了节点度中心性、接近中心性及介数中心性的信息，提出考虑节点重要性的CN、AA、RA链路预测相似性指标。在4个真实数据集上进行仿真实验，以AUC值作为链路预测精度评价指标，实验结果表明，改进的算法在4个数据集上的链路预测精度均高于共同邻居等对比算法，能够对复杂网络结构产生更精确的分析预测。

关键词: 复杂网络, 中心性, 相似性, 链路预测, 共同邻居