融合用户行为同步指标的链路预测研究

现有链路预测方法大多基于网络结构相似性及连边的权重特征，没有有效挖掘连边权重形成的时序信息。考虑到两个节点行为的时间同步性往往是由于两个节点存在链接造成的，因此在网络结构的重构研究中通常利用节点的行为同步性来反推它们之间是否存在链接关系。该文尝试将节点同步性信息这一网络重构的方法引入链路预测领域，提出一种网络拓扑相似性上融合节点行为同步指数的链路预测算法。经过两类6种真实网络数据的比较分析，发现该算法可有效提高链路预测准确率，相比现有方法，Precision指标提高了15.3%～68.2%。该研究不仅发现节点局域结构相似性和节点行为同步指数对链路预测的共同影响，也揭示了不同类别真实加权网络的内在结构和动态特征。

Link Prediction by Fusing Synchronization Index of User Behaviors

Most of existing methods of link prediction are based on the similarity of network structures and the weight of edges, but they do not effectively use temporal information of forming the weight of edges. The behavior synchronization of two nodes is often caused by the link relationship between them, so the behavior synchronization of nodes has been widely used in many researches of network structure reconstruction to conjecture whether there is a link relationship between any pair of nodes. In this study, we attempt to introduce node synchronization information into the field of link prediction, and propose a novel link prediction algorithm which integrates the synchronization index of node behaviors with network topological similarity. By analyzing and comparing two types of six real-life network data, the proposed method can effectively improve the accuracy of link prediction. Compared with the existing methods, the performance of precision can increase by 15.3% to 68.2%. This study not only finds the joint influence of local structure similarity and behavior synchronization index on link prediction, but also reveals intrinsic structures and dynamic characteristics of different types of real-life weighted networks.