一种时序有向网络中的链路预测方法

真实网络大多是有向的,且网络结构随时间动态变化,传统的链路预测方法大多适用于无向网络,其分析方法不能有效挖掘真实网络中的信息。针对以上问题,提出了一种基于归一化AA和LAS的时序有向的链路预测算法,该算法基于共同邻居、节点度属性及局部社团相似性,为每个链接分配时间影响因子并将其引入NALAS指标进行计算,考虑了网络有向性和网络历史结构的影响。在真实社会网络数据集上对该算法进行了仿真并与Salton、Jaccard等算法进行对比。结果表明,提出的算法与其他算法相比,预测精度得到了提高,说明该算法可以有效地在时序有向的社会网络中进行链路预测。

Link Prediction Method in Sequential Directed Network

Most real networks are directed, and the network structure changes dynamically with time. Traditional link prediction methods are mostly applicable to undirected networks, and their analysis methods cannot effectively mine information in real networks. Aiming at the above problems, this paper proposes a timing-directed link prediction algorithm based on normalized AA and LAS. Based on the common neighbor, node degree attribute and local community similarity, the algorithm assigns a time impact factor to each link and substitutes it into the NALAS indicator for calculation. It considers the impact of network directionality and network history structure. The algorithm is simulated on real social network datasets and compared with Salton and Jaccard algorithms. The results show that the proposed algorithm is improved compared with other algorithms, which indicates that the algorithm can effectively predict the link in a time-oriented social network.