Aiming at the problem that the existing majority link prediction algorithm has low accuracy, a link prediction algorithm based on the Normalized Common Neighbor and Local Clustering (NCNLC) similarity index is proposed based on the combination of global and local features.The similarity index of NCNLC attribute of the node is analyzed, and the cumulative influence factor is assigned to the joint between the nodes.Simulation results show that compared with the LAS index similarity algorithm, the proposed algorithm has higher prediction accuracy and can effectively predict the link in the temporal directed social network.%针对现有多数链路预测算法准确度较低的问题, 在网络全局与局部特征相结合基础上, 提出一种基于归一化共同邻居与局部聚类 (NCNLC) 相似性指标的链路预测算法.通过分析节点NCNLC属性的相似性指标, 为节点间的连边分配累积影响因子.仿真结果表明, 与LAS指标相似性算法相比, 该算法具有较高的预测准确度, 能够有效地进行时序有向社会网络中的链路预测.
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